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Stevenson, Emma; Rodríguez-Fernández, Víctor; Urrutxua, Hodei; Camacho, David
Benchmarking deep learning approaches for all-vs-all conjunction screening Journal Article
In: Advances in Space Research, 2023.
@article{nokey,
title = {Benchmarking deep learning approaches for all-vs-all conjunction screening},
author = {Emma Stevenson and Víctor Rodríguez-Fernández and Hodei Urrutxua and David Camacho},
doi = {https://doi.org/10.1016/j.asr.2023.01.036},
year = {2023},
date = {2023-01-23},
journal = {Advances in Space Research},
abstract = {The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.},
keywords = {},
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}
Stevenson, Emma; Rodríguez-Fernández, Víctor; Taillan, Christophe; Urrutxua, Hodei; Camacho, David
A deep learning-based framework for operational all-vs-all conjunction screening Inproceedings
In: 2nd International Stardust Conference (STARCON-2), ESTEC, the Netherlands, 2022.
@inproceedings{stevenson2022_starcon2,
title = {A deep learning-based framework for operational all-vs-all conjunction screening},
author = {Emma Stevenson and Víctor Rodríguez-Fernández and Christophe Taillan and Hodei Urrutxua and David Camacho},
year = {2022},
date = {2022-11-07},
booktitle = {2nd International Stardust Conference (STARCON-2)},
address = {ESTEC, the Netherlands},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei
Towards graph-based machine learning for conjunction assessment Inproceedings
In: 2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS), Maui, Hawaii, USA, 2022.
@inproceedings{stevenson2022_amos,
title = {Towards graph-based machine learning for conjunction assessment},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua},
year = {2022},
date = {2022-09-19},
urldate = {2022-09-19},
booktitle = {2022 Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS)},
address = {Maui, Hawaii, USA},
abstract = {In the face of increasing space traffic, the deployment of large constellations, and a growing debris field, identifying potentially catastrophic collisions is an increasingly daunting and computationally challenging task. In this work, we present a novel graph-based machine learning approach for detecting conjunctions between catalogued space objects to aid in this task. Modelling conjunction events as edges between pairs of object nodes, we introduce a graphical representation of the all-vs-all scenario (so-called as it considers conjunction events between all catalogued objects, both active and debris) that is able to profit from recent advancements in Graph Neural Networks, and make a step towards efficient, machine learning based conjunction assessment. For this, we develop a methodology to predict the existence of upcoming conjunction links over a given screening period, which we frame as a graph-to-graph link prediction task, and present some initial findings that demonstrate the learning potential of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Huertas-Tato, Javier; Martín, Alejandro; Camacho, David
SILT: Efficient transformer training for inter-lingual inference Journal Article
In: Expert Systems with Applications, vol. 200, pp. 116923, 2022, ISSN: 0957-4174.
@article{huertas-tato_silt_2022,
title = {SILT: Efficient transformer training for inter-lingual inference},
author = {Javier Huertas-Tato and Alejandro Martín and David Camacho},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422003578},
doi = {10.1016/j.eswa.2022.116923},
issn = {0957-4174},
year = {2022},
date = {2022-08-01},
urldate = {2022-08-01},
journal = {Expert Systems with Applications},
volume = {200},
pages = {116923},
abstract = {The ability of transformers to perform precision tasks such as question answering, Natural Language Inference (NLI) or summarizing, has enabled them to be ranked as one of the best paradigms to address Natural Language Processing (NLP) tasks. NLI is one of the best scenarios to test these architectures, due to the knowledge required to understand complex sentences and established relationships between a hypothesis and a premise. Nevertheless, these models suffer from the incapacity to generalize to other domains or from difficulties to face multilingual and interlingual scenarios. The leading pathway in the literature to address these issues involve designing and training extremely large architectures, but this causes unpredictable behaviors and establishes barriers which impede broad access and fine tuning. In this paper, we propose a new architecture called Siamese Inter-Lingual Transformer (SILT). This architecture is able to efficiently align multilingual embeddings for Natural Language Inference, allowing for unmatched language pairs to be processed. SILT leverages siamese pre-trained multi-lingual transformers with frozen weights where the two input sentences attend to each other to later be combined through a matrix alignment method. The experimental results carried out in this paper evidence that SILT allows to reduce drastically the number of trainable parameters while allowing for inter-lingual NLI and achieving state-of-the-art performance on common benchmarks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stevenson, Emma; Martinez, Riansares; Rodriguez-Fernandez, Victor; Camacho, David
Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning Inproceedings
In: 2022 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, Padua, Italy, 2022.
@inproceedings{9870215,
title = {Predicting the effects of kinetic impactors on asteroid deflection using end-to-end deep learning},
author = {Emma Stevenson and Riansares Martinez and Victor Rodriguez-Fernandez and David Camacho},
doi = {10.1109/CEC55065.2022.9870215},
year = {2022},
date = {2022-07-18},
urldate = {2022-07-18},
booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)},
pages = {1-8},
address = {Padua, Italy},
abstract = {One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei; Camacho, David
Deep learning for all-vs-all conjunction detection Inproceedings
In: 5th Workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KePASSA), Logroño, Spain, 2022.
@inproceedings{stevenson2022_kepassa,
title = {Deep learning for all-vs-all conjunction detection},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and David Camacho},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {5th Workshop on Key Topics in Orbit Propagation Applied to Space Situational Awareness (KePASSA)},
address = {Logroño, Spain},
abstract = {This paper explores the use of different deep learning techniques for detecting conjunction events in an efficient and accurate way for improved space situational awareness. Framing the problem as a machine learning classification task, we present the performance of different data representations and model architectures on a realistic all-vs-all dataset generated using the CNES BAS3E space surveillance simulation framework, and compare the approaches to operationally used classical filters in screening performance and computational efficiency. Finally, we also investigate a novel methodology for improving the performance and generalisation ability of the models using a pre-trained orbit model, ORBERT, based on self-supervised learning techniques.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Huertas-García, Álvaro; Martín, Alejandro; Huertas-Tato, Javier; Camacho, David
Exploring Dimensionality Reduction Techniques in Multilingual Transformers Miscellaneous
CoRR, 2022.
@misc{nokey,
title = {Exploring Dimensionality Reduction Techniques in Multilingual Transformers},
author = {Álvaro Huertas-García and Alejandro Martín and Javier Huertas-Tato and David Camacho},
url = {https://doi.org/10.48550/arxiv.2204.08415},
doi = {10.48550/ARXIV.2204.08415},
year = {2022},
date = {2022-04-18},
urldate = {2022-04-18},
abstract = {Both in scientific literature and in industry,, Semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Language Understanding tasks is unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. The growing need to provide more complex models implementing all these features has been accompanied by an increase in their size, without being conservative in the number of dimensions required. This paper aims to give a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual Siamese Transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the pre-trained version of several models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction in the number of dimensions of 91.58%±2.59% and 54.65%±32.20%, respectively. This work has also considered the consequences of dimensionality reduction for visualization purposes. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for highly demanding NLP tasks },
howpublished = {CoRR},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Minisci, Edmondo; Camacho, David
A deep learning approach to solar radio flux forecasting Journal Article
In: Acta Astronautica, vol. 193, pp. 595-606, 2022, ISSN: 0094-5765.
@article{STEVENSON2022595,
title = {A deep learning approach to solar radio flux forecasting},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Edmondo Minisci and David Camacho},
url = {https://www.sciencedirect.com/science/article/pii/S009457652100415X},
doi = {https://doi.org/10.1016/j.actaastro.2021.08.004},
issn = {0094-5765},
year = {2022},
date = {2022-01-01},
journal = {Acta Astronautica},
volume = {193},
pages = {595-606},
abstract = {The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Panizo-Lledot, Angel; Pedemonte, Mart'in; Bello-Orgaz, Gema; Camacho, David
Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs Journal Article
In: IEEE Access, vol. 10, pp. 27611–27629, 2022.
@article{panizo2022addressing,
title = {Addressing Evolutionary-Based Dynamic Problems: A New Methodology for Evaluating Immigrants Strategies in MOGAs},
author = {Angel Panizo-Lledot and Mart'in Pedemonte and Gema Bello-Orgaz and David Camacho},
doi = {10.1109/ACCESS.2022.3156944},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Access},
volume = {10},
pages = {27611--27629},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Urrutxua, Hodei; Morand, Vincent; Camacho, David
Self-supervised machine learning based approach to orbit modelling applied to space traffic management Inproceedings
In: 11th International Association for the Advancement of Space Safety Conference (IAASS), (Virtual), Osaka, Japan, 2021.
@inproceedings{stevenson2021_iaass,
title = {Self-supervised machine learning based approach to orbit modelling applied to space traffic management},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Hodei Urrutxua and Vincent Morand and David Camacho},
year = {2021},
date = {2021-10-01},
booktitle = {11th International Association for the Advancement of Space Safety Conference (IAASS)},
address = {(Virtual), Osaka, Japan},
abstract = {This paper presents a novel methodology for improving the performance of machine learning based space traffic management tasks through the use of a pre-trained orbit model. Taking inspiration from BERT-like self-supervised language models in the field of natural language processing, we introduce ORBERT, and demonstrate the ability of such a model to leverage large quantities of readily available orbit data to learn meaningful representations that can be used to aid in downstream tasks. As a proof of concept of this approach we consider the task of all vs. all conjunction screening, phrased here as a machine learning time series classification task. We show that leveraging unlabelled orbit data leads to improved performance, and that the proposed approach can be particularly beneficial for tasks where the availability of labelled data is limited.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Huertas-García, Álvaro; Huertas-Tato, Javier; Martín, Alejandro; Camacho, David
CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification Inproceedings
In: Conference and Labs of the Evaluation Forum (CLEF) Working Notes, pp. 520–530, 2021.
@inproceedings{huertas-garcia_civic-upm_2021,
title = {CIVIC-UPM at CheckThat! 2021: Integration of Transformers in Misinformation Detection and Topic Classification},
author = {Álvaro Huertas-García and Javier Huertas-Tato and Alejandro Martín and David Camacho},
url = {http://ceur-ws.org/Vol-2936/paper-41.pdf},
year = {2021},
date = {2021-05-24},
urldate = {2021-05-24},
booktitle = {Conference and Labs of the Evaluation Forum (CLEF) Working Notes},
pages = {520--530},
abstract = {Online Social Networks (OSNs) growth enables and amplifies the quick spread of harmful, manipulative and false information that influence public opinion while sow conflict on social or political issues. Therefore, the development of tools to detect malicious actors and to identify low-credibility information and misinformation sources is a new crucial challenge in the ever-evolving field of Artificial Intelligence. The scope of this paper is to present a Natural Language Processing (NLP) approach that uses Doc2Vec and different state-of-the-art transformer-based models for the CLEF2021 Checkthat! lab Task 3. Through this approach, the results show that it is possible to achieve 41.43% macro-average F1-score in the misinformation detection (Task A) and 67.65% macro-average F1-score in the topic classification (Task B).},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Javadi, Mahrokh; Ramirez-Atencia, Cristian; Mostaghim, Sanaz
Combining manhattan and crowding distances in decision space for multimodal multi-objective optimization problems Incollection
In: Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, vol. 55, pp. 131–145, Springer, Cham, 2021, ISBN: 978-3-030-57422-2.
@incollection{javadi2021combining,
title = {Combining manhattan and crowding distances in decision space for multimodal multi-objective optimization problems},
author = {Mahrokh Javadi and Cristian Ramirez-Atencia and Sanaz Mostaghim},
doi = {10.1007/978-3-030-57422-2_9},
isbn = {978-3-030-57422-2},
year = {2021},
date = {2021-01-01},
booktitle = {Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences},
volume = {55},
pages = {131--145},
publisher = {Springer, Cham},
series = {Computational Methods in Applied Sciences},
abstract = {This paper presents a new variant of the Non-dominated Sorting Genetic Algorithm to solve Multimodal Multi-objective optimization problems. We introduce a novel method to augment the diversity of solutions in decision space by combining the Manhattan and crowding distance. In our experiments, we use six test problems with different levels of complexity to examine the performance of our proposed algorithm. The results are compared with NSGA-II and NSGA-II-WSCD algorithms. Using IGDX and IGD performance indicators, we demonstrate the superiority of our proposed method over the rest of competitors to provide a better approximation of the Pareto Set (PS) while not getting much worse results in objective space.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Guamán, Daniel; Delgado, Maria Soledad; Pérez, Jennifer
Classifying Model-View-Controller Software Applications Using Self-Organizing Maps Journal Article
In: IEEE Access, vol. 9, pp. 45201-45229, 2021, ISBN: 2169-3536.
@article{9380344,
title = {Classifying Model-View-Controller Software Applications Using Self-Organizing Maps},
author = {Daniel Guamán and Maria Soledad Delgado and Jennifer Pérez},
doi = {10.1109/ACCESS.2021.3066348},
isbn = {2169-3536},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {45201-45229},
abstract = {The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Delgado, Maria Soledad; Morán, Federico; José, José Carlos San; Burgos, Daniel
Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks Journal Article
In: IEEE Access, vol. 9, pp. 132592-132608, 2021, ISSN: 2169-3536 .
@article{9546766,
title = {Analysis of Students’ Behavior Through User Clustering in Online Learning Settings, Based on Self Organizing Maps Neural Networks},
author = {Maria Soledad Delgado and Federico Morán and José Carlos San José and Daniel Burgos},
doi = {10.1109/ACCESS.2021.3115024},
issn = {2169-3536 },
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE Access},
volume = {9},
pages = {132592-132608},
abstract = {An accurate analysis of user behaviour in online learning environments is a useful means of early follow up of students, so that they can be better supported to improve their performance and achieve the expected competences. However, that task becomes challenging due to the massive data that learning management systems store and categories. With the COVID-19 pandemic still on-going, face-to-face learning settings have migrate into online and blended ones, meaning an increase of online students and teachers in need for a tailored and effective support to their needs. A novel unsupervised clustering technique based on the Self-Organizing Map (SOM) artificial neural network model is used in this research to analyse 1,709,189 records of online students enrolled from 2015 to 2019 at Universidad Internacional de La Rioja (UNIR), a fully online Higher Education institution. SOM performs a precise and diverse user clustering based on those records. Results highlight that specific clusters are linked to the intake average profile at the university, with a clear relation between user interaction and a higher performance. Further, results show that, out of a targeted desk research compared to the analysis in this paper, face-to-face and online settings are connected through the methodological approach beyond the technology-based environment, which presents a similar behaviour in both contexts},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Stevenson, Emma; Rodriguez-Fernandez, Victor; Minisci, Edmondo; Camacho, David
A deep learning approach to space weather proxy forecasting for orbital prediction Inproceedings
In: 71st International Astronautical Congress (IAC), The CyberSpace Edition, 2020.
@inproceedings{stevenson2020_iac,
title = {A deep learning approach to space weather proxy forecasting for orbital prediction},
author = {Emma Stevenson and Victor Rodriguez-Fernandez and Edmondo Minisci and David Camacho},
url = {http://oa.upm.es/64345/},
year = {2020},
date = {2020-10-01},
booktitle = {71st International Astronautical Congress (IAC)},
address = {The CyberSpace Edition},
abstract = {The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. This has fundamental implications both in the short term, in the day-to-day management of operational spacecraft, and in the mid-to-long term, in determining satellite orbital lifetime. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Javadi, Mahrokh; Ramirez-Atencia, Cristian; Mostaghim, Sanaz
A novel grid-based crowding distance for multimodal multi-objective optimization Inproceedings
In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, IEEE 2020, ISBN: 978-1-7281-6929-3.
@inproceedings{javadi2020novel,
title = {A novel grid-based crowding distance for multimodal multi-objective optimization},
author = {Mahrokh Javadi and Cristian Ramirez-Atencia and Sanaz Mostaghim},
doi = {10.1109/CEC48606.2020.9185835},
isbn = {978-1-7281-6929-3},
year = {2020},
date = {2020-09-03},
booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},
pages = {1--8},
organization = {IEEE},
abstract = {Preserving diversity in decision space plays an important role in Multimodal Multi-objective Optimization problems (MMOPs). Due to the lack of mechanisms to keep different solutions with the same fitness value, most of the available Multi-objective Evolutionary Algorithms (MOEAs) perform poorly when applied to MMOPs. To deal with these problems, this paper proposes a novel method for diversity preserving in the decision space. To this end, the concept of grid-based crowding distance for decision space is introduced. Furthermore, to keep a good diversity of solutions in both decision and objective spaces, we propose different frameworks by combining this method with crowding distance in decision space, crowding distance in objective space, and the weighted sum of both crowding distances. In order to evaluate the performance of these frameworks, we integrate them into the diversity preserving part of the NSGA-II algorithm, and compare them with the NSGA-II (as the baseline algorithm) and the state-of-the-art multimodal multi-objective optimization algorithms on ten different MMOPs with different levels of complexity.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Benecke, Tobias; Mostaghim, Sanaz
T-EA: A traceable evolutionary algorithm Inproceedings
In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8, IEEE 2020, ISBN: 978-1-7281-6929-3.
@inproceedings{ramirez2020tea,
title = {T-EA: A traceable evolutionary algorithm},
author = {Cristian Ramirez-Atencia and Tobias Benecke and Sanaz Mostaghim},
doi = {10.1109/CEC48606.2020.9185615},
isbn = {978-1-7281-6929-3},
year = {2020},
date = {2020-09-03},
urldate = {2020-09-03},
booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)},
pages = {1--8},
organization = {IEEE},
abstract = {In this paper, the influence of the initial population into successive generations in Evolutionary Algorithms (EAs) is studied as a problem-independent approach. For this purpose, the Traceable Evolutionary Algorithm (T-EA) is proposed. This algorithm keeps track of the influence of the individuals from the initial population over the generations of the algorithm. The algorithm has been implemented for both bit-string and integer vector representations. In addition, in order to study the general influence of each individual, new impact factor metrics have been proposed. In this way, we aim to provide tools to measure the influence of initial individuals on the final solutions. As a proof of concept, three classical optimization problems (One Max, 0/1 Knapsack and Unbounded Knapsack problems) are used. We provide a framework that allows to explain why some individuals in the initial population work better than others in relation with the corresponding fitness values.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Panizo-LLedot, Angel; Bello-Orgaz, Gema; Camacho, David
A multi-objective genetic algorithm for detecting dynamic communities using a local search driven immigrant’s scheme Journal Article
In: Future Generation Computer Systems, vol. 110, pp. 960–975, 2020.
@article{panizo2020multi,
title = {A multi-objective genetic algorithm for detecting dynamic communities using a local search driven immigrant’s scheme},
author = {Angel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},
doi = {10.1016/j.future.2019.10.041},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Future Generation Computer Systems},
volume = {110},
pages = {960--975},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pedemonte, Martín; Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Camacho, David
Exploring multi-objective cellular genetic algorithms in community detection problems Inproceedings
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 223–235, Springer 2020.
@inproceedings{pedemonte2020exploring,
title = {Exploring multi-objective cellular genetic algorithms in community detection problems},
author = {Martín Pedemonte and Ángel Panizo-LLedot and Gema Bello-Orgaz and David Camacho},
doi = {10.1007/978-3-030-62365-4_22},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {223--235},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A revision on multi-criteria decision making methods for multi-UAV mission planning support Journal Article
In: Expert Systems with Applications, vol. 160, pp. 113708, 2020, ISSN: 0957-4174.
@article{RAMIREZATENCIA2020113708,
title = {A revision on multi-criteria decision making methods for multi-UAV mission planning support},
author = {Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho},
url = {https://www.sciencedirect.com/science/article/pii/S0957417420305327},
doi = {https://doi.org/10.1016/j.eswa.2020.113708},
issn = {0957-4174},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Expert Systems with Applications},
volume = {160},
pages = {113708},
abstract = {Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the mission planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Camacho, David; Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Gonzalez-Pardo, Antonio; Cambria, Erik
The four dimensions of social network analysis: An overview of research methods, applications, and software tools Journal Article
In: Information Fusion, vol. 63, pp. 88–120, 2020.
@article{camacho2020four,
title = {The four dimensions of social network analysis: An overview of research methods, applications, and software tools},
author = {David Camacho and Ángel Panizo-LLedot and Gema Bello-Orgaz and Antonio Gonzalez-Pardo and Erik Cambria},
doi = {10.1016/j.inffus.2020.05.009},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Information Fusion},
volume = {63},
pages = {88--120},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Camacho, David
Constrained multi-objective optimization for multi-UAV planning Journal Article
In: Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 6, pp. 2467–2484, 2019, ISSN: 1868-5145.
@article{ramirez2019constrained,
title = {Constrained multi-objective optimization for multi-UAV planning},
author = {Cristian Ramirez-Atencia and David Camacho},
doi = {10.1007/s12652-018-0930-0},
issn = {1868-5145},
year = {2019},
date = {2019-06-01},
journal = {Journal of Ambient Intelligence and Humanized Computing},
volume = {10},
number = {6},
pages = {2467--2484},
publisher = {Springer Berlin Heidelberg},
abstract = {Over the last decade, developments in unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Ser, Javier Del; Camacho, David
Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning Journal Article
In: Swarm and Evolutionary Computation, vol. 44, pp. 480–495, 2019, ISSN: 2210-6502.
@article{ramirez2019weighted,
title = {Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning},
author = {Cristian Ramirez-Atencia and Javier Del Ser and David Camacho},
doi = {10.1016/j.swevo.2018.06.005},
issn = {2210-6502},
year = {2019},
date = {2019-02-01},
journal = {Swarm and Evolutionary Computation},
volume = {44},
pages = {480--495},
publisher = {Elsevier},
abstract = {Management and mission planning over a swarm of unmanned aerial vehicle (UAV) remains to date as a challenging research trend in what regards to this particular type of aircrafts. These vehicles are controlled by a number of ground control station (GCS), from which they are commanded to cooperatively perform different tasks in specific geographic areas of interest. Mathematically the problem of coordinating and assigning tasks to a swarm of UAV can be modeled as a constraint satisfaction problem, whose complexity and multiple conflicting criteria has hitherto motivated the adoption of multi-objective solvers such as multi-objective evolutionary algorithm (MOEA). The encoding approach consists of different alleles representing the decision variables, whereas the fitness function checks that all constraints are fulfilled, minimizing the optimization criteria of the problem. In problems of high complexity involving several tasks, UAV and GCS, where the space of search is huge compared to the space of valid solutions, the convergence rate of the algorithm increases significantly. To overcome this issue, this work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning using weighted random strategies that focus the search on potentially better regions of the solution space. Extensive experimental results over a diverse range of scenarios evince the benefits of the proposed approach, which notably improves this convergence rate with respect to a naïve MOEA approach.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Delgado, Maria Soledad; Moreno, Miguel; Vázquez, Luis; Martín-Gago, José A.; Briones, Carlos
Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks Journal Article
In: 2019.
@article{<LineBreak> 10261_210788,
title = {Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks},
author = {Maria Soledad Delgado and Miguel Moreno and Luis Vázquez and José A. Martín-Gago and Carlos Briones},
doi = {10.1109/ACCESS.2019.2950984},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
organization = {This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) funded by the EU through the FEDER Programme under Grant BIO2016-79618-R and Grant MAT2017-85089-C2-1-R, in part by the Spanish State Research Agency (AEI) through the Unidad de Excelencia María de Maeztu-Centro de Astrobiología (CSIC-INTA) under Project MDM-2017-0737, and in part by the Comunidad de Madrid under Grant S2018/NMT-4349.},
abstract = {[EN] Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hernández, Alfonso; Panizo-LLedot, Ángel; Camacho, David
An ensemble algorithm based on deep learning for tuberculosis classification Inproceedings
In: International conference on intelligent data engineering and automated learning, pp. 145–154, Springer 2019.
@inproceedings{hernandez2019ensemble,
title = {An ensemble algorithm based on deep learning for tuberculosis classification},
author = {Alfonso Hernández and Ángel Panizo-LLedot and David Camacho},
doi = {10.1007/978-3-030-33607-3_17},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {International conference on intelligent data engineering and automated learning},
pages = {145--154},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Osaba, Eneko; Ser, Javier Del; Panizo-LLedot, Angel; Camacho, David; Galvez, Akemi; Iglesias, Andres
Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams Inproceedings
In: Proceedings of the genetic and evolutionary computation conference companion, pp. 1329–1335, 2019.
@inproceedings{osaba2019combining,
title = {Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams},
author = {Eneko Osaba and Javier Del Ser and Angel Panizo-LLedot and David Camacho and Akemi Galvez and Andres Iglesias},
doi = {10.1145/3319619.3326831},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {Proceedings of the genetic and evolutionary computation conference companion},
pages = {1329--1335},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian
Automated mission planning and decision support systems for multiple unmanned aerial vehicles PhD Thesis
Universidad Autónoma de Madrid, 2018.
@phdthesis{ramirez2018automated,
title = {Automated mission planning and decision support systems for multiple unmanned aerial vehicles},
author = {Cristian Ramirez-Atencia},
url = {http://hdl.handle.net/10486/686590},
year = {2018},
date = {2018-10-22},
school = {Universidad Autónoma de Madrid},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A multi-criteria decision support system for multi-UAV mission planning Incollection
In: Data Science and Knowledge Engineering for Sensing Decision Support, vol. 11, pp. 1083–1090, World Scientific, 2018, ISBN: 978-981-3273-22-1.
@incollection{ramirez2018multi,
title = {A multi-criteria decision support system for multi-UAV mission planning},
author = {Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho},
doi = {10.1142/9789813273238_0137},
isbn = {978-981-3273-22-1},
year = {2018},
date = {2018-10-01},
booktitle = {Data Science and Knowledge Engineering for Sensing Decision Support},
volume = {11},
pages = {1083--1090},
publisher = {World Scientific},
series = {World Scientific Proceedings Series on Computer Engineering and Information Science},
abstract = {The Multi-UAV Mission Planning problem is focused on the search of a set of solutions that satisfy several constraints on the mission scenario and has some variables to be optimized, such as the makespan, the cost of the mission or the risk. Thus, there could exist a large number of solutions to the problem. It turns a big issue for the operator to select the final solution to execute among the many obtained. In order to reduce the operator workload, this work proposes a Multi-Criteria Decision Support System, which consists of a ranking function that sorts the solutions obtained. Several ranking functions have been tested in real mission scenarios with different operator profiles. Expert operators have evaluated the solutions returned in order to compare the different ranking systems and demonstrate the usefulness of the proposed approach.},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Panizo-LLedot, Ángel; Bello-Orgaz, Gema; Carnero, Mercedes; Hernández, José; Sánchez, Mabel; Camacho, David
An Artificial Bee Colony algorithm for optimizing the design of sensor networks Inproceedings
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 316–324, Springer 2018.
@inproceedings{panizo2018artificial,
title = {An Artificial Bee Colony algorithm for optimizing the design of sensor networks},
author = {Ángel Panizo-LLedot and Gema Bello-Orgaz and Mercedes Carnero and José Hernández and Mabel Sánchez and David Camacho},
doi = {10.1007/978-3-030-03496-2_35},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {316--324},
organization = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Camacho-Gómez, C.; Wang, X.; Pereira, E.; Díaz, I. M.; Salcedo-Sanz, Sancho
Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm Journal Article
In: Engineering Structures, vol. 157, pp. 14-26, 2018, ISSN: 0141-0296.
@article{CAMACHOGOMEZ201814,
title = {Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm},
author = {C. Camacho-Gómez and X. Wang and E. Pereira and I. M. Díaz and Sancho Salcedo-Sanz},
url = {https://www.sciencedirect.com/science/article/pii/S0141029617311719},
doi = {https://doi.org/10.1016/j.engstruct.2017.12.002},
issn = {0141-0296},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Engineering Structures},
volume = {157},
pages = {14-26},
abstract = {Active vibration control (AVC) via inertial-mass actuators is a viable technique to mitigate human-induced vibrations in civil structures. A multi-input multi-output (MIMO) AVC has been previously proposed in the literature to simultaneously find the sensor/actuator pairs’ optimal placements and tune the control gains. However, the method involved local gradient-based methods, which is not affordable when the number of possible locations of actuators is large. In this case, the computation time to obtain a local solution may be huge and unaffordable, which limits the number of test points and/or actuators/sensors considered. This paper proposes an alternative approach based on a recently proposed meta-heuristic, the Coral Reefs Optimization (CRO) algorithm. More concretely, an enhanced version of the CRO is considered, the Coral Reefs Optimization with Substrate Layer (CRO-SL). The CRO-SL is a competitive co-evolution algorithm in which different exploration procedures are jointly evolved within a single population of potential solutions to the problem. The proposed algorithm is thus able to promote competition among different search methods to solve hard optimization problems. In terms of structural design, this work provides an important step to improve the applicability of AVC systems to real complex structures (with a large number of vibration modes and/or with a large number of test points) by achieving global optimum designs with affordable computation time. A finite element model of a real complex floor structure is used to illustrate the contributions of this paper.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; R-Moreno, Maria D; Camacho, David
Handling swarm of UAVs based on evolutionary multi-objective optimization Journal Article
In: Progress in Artificial Intelligence, vol. 6, iss. 3, pp. 263-274, 2017, ISSN: 2192-6352.
@article{Ramirez-Atencia2017,
title = {Handling swarm of UAVs based on evolutionary multi-objective optimization},
author = {Cristian Ramirez-Atencia and Maria D R-Moreno and David Camacho},
url = {http://link.springer.com/10.1007/s13748-017-0123-7},
doi = {10.1007/s13748-017-0123-7},
issn = {2192-6352},
year = {2017},
date = {2017-09-01},
urldate = {2017-01-01},
journal = {Progress in Artificial Intelligence},
volume = {6},
issue = {3},
pages = {263-274},
publisher = {Springer Berlin Heidelberg},
abstract = {The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms Journal Article
In: Soft Computing, vol. 21, iss. 17, pp. 4883-4900, 2017, ISSN: 1432-7643; 1433-7479.
@article{Ramirez-Atencia2016c,
title = {Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
doi = {10.1007/s00500-016-2376-7},
issn = {1432-7643; 1433-7479},
year = {2017},
date = {2017-09-01},
urldate = {2016-01-01},
journal = {Soft Computing},
volume = {21},
issue = {17},
pages = {4883-4900},
publisher = {Springer Berlin Heidelberg},
abstract = {Due to recent booming of unmanned air vehicles (UAVs) technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from ground control stations (GCSs) where human operators use rudimentary systems. This paper presents a new multi-objective genetic algorithm for solving complex mission planning problems involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a constraint satisfaction problem to check whether solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets, optimizing different variables of the mission, such as the makespan, the fuel consumption, and distance. Experimental results show that the new algorithm is able to obtain good solutions; however, as the problem becomes more complex, the optimal solutions also become harder to find.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Mostaghim, Sanaz; Camacho, David
A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems Inproceedings
In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1216–1223, ACM, Berlin, Germany, 2017, ISBN: 978-1-4503-4920-8.
@inproceedings{Ramirez-Atencia2017b,
title = {A Knee Point Based Evolutionary Multi-objective Optimization for Mission Planning Problems},
author = {Cristian Ramirez-Atencia and Sanaz Mostaghim and David Camacho},
url = {http://doi.acm.org/10.1145/3071178.3071319},
doi = {10.1145/3071178.3071319},
isbn = {978-1-4503-4920-8},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {1216--1223},
publisher = {ACM},
address = {Berlin, Germany},
series = {GECCO '17},
abstract = {The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
A study on performance metrics and clustering methods for analyzing behavior in UAV operations Journal Article
In: Journal of Intelligent and Fuzzy Systems, vol. 32, no. 2, pp. 1307–1319, 2017.
@article{DBLP:journals/jifs/Rodriguez-Fernandez17,
title = {A study on performance metrics and clustering methods for analyzing behavior in UAV operations},
author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},
url = {http://dx.doi.org/10.3233/JIFS-169129},
doi = {10.3233/JIFS-169129},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Intelligent and Fuzzy Systems},
volume = {32},
number = {2},
pages = {1307--1319},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
Analysing temporal performance profiles of UAV operators using time series clustering Journal Article
In: Expert Systems with Applications, vol. 70, pp. 103–118, 2017, ISSN: 0957-4174.
@article{rodriguez20171Analysing,
title = {Analysing temporal performance profiles of UAV operators using time series clustering},
author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},
url = {http://www.sciencedirect.com/science/article/pii/S0957417416305851},
doi = {http://dx.doi.org/10.1016/j.eswa.2016.10.044},
issn = {0957-4174},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Expert Systems with Applications},
volume = {70},
pages = {103--118},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Delgado, Maria Soledad; Higuera, Clara; Calle-Espinosa, Jorge; Morán, Federico; Montero, Francisco
A SOM prototype-based cluster analysis methodology Journal Article
In: Expert Systems with Applications, vol. 88, pp. 14-28, 2017, ISSN: 0957-4174.
@article{DELGADO201714,
title = {A SOM prototype-based cluster analysis methodology},
author = {Maria Soledad Delgado and Clara Higuera and Jorge Calle-Espinosa and Federico Morán and Francisco Montero},
url = {https://www.sciencedirect.com/science/article/pii/S0957417417304396},
doi = {https://doi.org/10.1016/j.eswa.2017.06.022},
issn = {0957-4174},
year = {2017},
date = {2017-01-01},
journal = {Expert Systems with Applications},
volume = {88},
pages = {14-28},
abstract = {Data clustering is aimed at finding groups of data that share common hidden properties. These kinds of techniques are especially critical at early stages of data analysis where no information about the dataset is available. One of the mayor shortcomings of the clustering algorithms is the difficulty for non-experts users to configure them and, in some cases, interpret the results. In this work a computational approach with a two-layer structure based on Self-Organizing Map (SOM) is presented for cluster analysis. In the first level, a quantization of the data samples using topology-preserving metrics to automatically determine the number of units in the SOM is proposed. In the second level the obtained SOM prototypes are clustered by means of a connectivity analysis to explore the quality of the partitioning with different number of clusters. The most important benefit of this two-layer procedure is that computational load decreases considerably in comparison with data based clustering methods, making it possible to cluster large data sets and to consider several different clustering alternatives in a limited time. This methodology produces a two-dimensional map representation of the, usually, high dimensional input space, along with quantitative information on viable clustering alternatives, which facilitates the exploration of the possible partitions in a dataset. The efficiency and interpretation of the methodology is illustrated by its application to artificial, benchmark and real complex biological datasets. The experimental results demonstrate the ability of the method to identify possible segmentations in a dataset, compared to algorithms that only yield a single clustering solution. The proposed algorithm tackles the intrinsic limitations of SOM and the parameter settings associated with the clustering methodology, without requiring the number of clusters or the SOM architecture as a prerequisite, among others. This way, it makes possible its application even by researchers with a limited expertise in machine learning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Martín, Alejandro; Lara-Cabrera, Raúl; Fuentes-Hurtado, Félix; Naranjo, Valery; Camacho, David
EvoDeep: a new Evolutionary approach for automatic Deep Neural Networks parametrisation Journal Article
In: Journal of Parallel and Distributed Computing, 2017.
@article{martin2017evodeep,
title = {EvoDeep: a new Evolutionary approach for automatic Deep Neural Networks parametrisation},
author = {Alejandro Martín and Raúl Lara-Cabrera and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Parallel and Distributed Computing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Martín, Alejandro; Fuentes-Hurtado, Félix; Naranjo, Valery; Camacho, David
Evolving deep neural networks architectures for Android malware classification Inproceedings
In: Evolutionary Computation (CEC), 2017 IEEE Congress on, pp. 1659–1666, IEEE 2017.
@inproceedings{martin2017evolving,
title = {Evolving deep neural networks architectures for Android malware classification},
author = {Alejandro Martín and Félix Fuentes-Hurtado and Valery Naranjo and David Camacho},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Evolutionary Computation (CEC), 2017 IEEE Congress on},
pages = {1659--1666},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems Inproceedings
In: XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016), pp. 305–314, 2016.
@inproceedings{Ramirez-Atencia2016a,
title = {A Weighted Penalty Fitness for a Hybrid MOGA-CSP to solve Mission Planning Problems},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/A-Weighted-Penalty-Fitness-for-a-Hybrid-MOGA-CSP-to-solve-Mission-Planning-Problems.pdf},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {XI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB 2016)},
pages = {305--314},
abstract = {Unmanned Aerial Vehicles (UAVs) are currently booming due to their high number of potential applications. In Mission Planning problems, several tasks must be performed by a team of UAVs, under the supervision of one or more Ground Control Stations (GCSs). In our approach, we have modelled the problem as a Constraint Satisfaction Problem (CSP), and solved it using a Multi-Objective Genetic Algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission such as the fuel consumption or the makespan. In addition, the fitness function takes a new consideration when solutions are not valid. It uses the number of constraints fulfilled for each solution as a weighted penalty function. In this way, the number of constraints fulfilled is maximized in the elitism phase of the MOGA. Results show that the approach outperforms the convergence with respect to previous results.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning Inproceedings
In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, ISBN: 978-1-5090-4240-1, 978-1-5090-4241-8.
@inproceedings{Ramirez-Atencia2016b,
title = {MOGAMR: A Multi-Objective Genetic Algorithm for Real-Time Mission Replanning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
doi = {10.1109/SSCI.2016.7850235},
isbn = {978-1-5090-4240-1, 978-1-5090-4241-8},
year = {2016},
date = {2016-01-01},
booktitle = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
abstract = {From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of MultiUAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to realtime Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an offlline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Martín, Alejandro; Menéndez, Héctor D; Camacho, David
MOCDroid: multi-objective evolutionary classifier for Android malware detection Journal Article
In: Soft Computing, pp. 1–11, 2016.
@article{martin2016mocdroid,
title = {MOCDroid: multi-objective evolutionary classifier for Android malware detection},
author = {Alejandro Martín and Héctor D Menéndez and David Camacho},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Soft Computing},
pages = {1--11},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Suárez, Óscar Manuel Losada; Rodríguez-Fernández, Víctor; Ramírez-Atencia, Cristian; Camacho, David
Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos Inproceedings
In: 3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016), pp. 135–146, CEUR Workshop, Barcelona, Spain, 2016, ISSN: 16130073.
@inproceedings{LosadaSuarez2016,
title = {Desarrollo de una plataforma basada en Unity3D para la aplicación de IA en videojuegos},
author = {Óscar Manuel Losada Suárez and Víctor Rodríguez-Fernández and Cristian Ramírez-Atencia and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2017/03/Desarrollo-de-una-plataforma-basada-en-Unity3D-para-la-aplicación-de-IA-en-videojuegos.pdf},
issn = {16130073},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {3rd Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016)},
volume = {1682},
pages = {135--146},
publisher = {CEUR Workshop},
address = {Barcelona, Spain},
abstract = {La utilización intensiva de diferentes técnicas relacionadas con la Inteligencia Artificial (IA) en el área de los videojuegos ha demostrado ser una necesidad para el campo. El uso de estas técnicas permite dotar de una mayor flexibilidad y adaptabilidad a los juegos que es muy apreciada por los jugadores. Temas como la generación procedimental de contenido, la creación de agentes que puedan jugar a un videojuego de forma competente, o de agentes cuya conducta sea indistinguible de la de un jugador humano atraen a una cantidad creciente de investigadores. El objetivo de este trabajo es la presentación de una plataforma basada en el motor Unity3D que permita de manera simple la integración y prueba de algoritmos de IA. La plataforma ofrecerá como nuevas características, adicionales a las ya disponibles en la actualidad, la utilización de un entorno 3D, el desarrollo de un juego innovador (basado en múltiples agentes), y la exploración de aspectos de juego como el análisis del terreno, la cooperación entre agentes independientes y heterogéneos, la comunicación de información entre los mismos y la formación de jerarquías.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Delgado, Maria Soledad; Morán, Federico; Mora, Antonio; Merelo, Juan Julián; Briones, Carlos
A novel representation of genomic sequences for taxonomic clustering and visualization by means of self-organizing maps Journal Article
In: Bioinformatics, vol. 31, no. 5, pp. 736-744, 2015, ISSN: 1367-4803.
@article{10.1093/bioinformatics/btu708,
title = {A novel representation of genomic sequences for taxonomic clustering and visualization by means of self-organizing maps},
author = {Maria Soledad Delgado and Federico Morán and Antonio Mora and Juan Julián Merelo and Carlos Briones},
url = {https://doi.org/10.1093/bioinformatics/btu708},
doi = {10.1093/bioinformatics/btu708},
issn = {1367-4803},
year = {2015},
date = {2015-03-01},
urldate = {2015-03-01},
journal = {Bioinformatics},
volume = {31},
number = {5},
pages = {736-744},
abstract = {Motivation: Self-organizing maps (SOMs) are readily available bioinformatics methods for clustering and visualizing high-dimensional data, provided that such biological information is previously transformed to fixed-size, metric-based vectors. To increase the usefulness of SOM-based approaches for the analysis of genomic sequence data, novel representation methods are required that automatically and bijectively transform aligned nucleotide sequences into numeric vectors, dealing with both nucleotide ambiguity and gaps derived from sequence alignment.Results: Six different codification variants based on Euclidean space, just like SOM processing, have been tested using two SOM models: the classical Kohonen’s SOM and growing cell structures. They have been applied to two different sets of sequences: 32 sequences of small sub-unit ribosomal RNA from organisms belonging to the three domains of life, and 44 sequences of the reverse transcriptase region of the pol gene of human immunodeficiency virus type 1 belonging to different groups and sub-types. Our results show that the most important factor affecting the accuracy of sequence clustering is the assignment of an extra weight to the presence of alignment-derived gaps. Although each of the codification variants shows a different level of taxonomic consistency, the results are in agreement with sequence-based phylogenetic reconstructions and anticipate a broad applicability of this codification method.Contact:sole@eui.upm.esSupplementary information:Supplementary Data are available at Bioinformatics online.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, María D; Camacho, David
A Hybrid MOGA-CSP for Multi-UAV Mission Planning Inproceedings
In: Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pp. 1205–1208, ACM 2015.
@inproceedings{ramirez2015hybrid,
title = {A Hybrid MOGA-CSP for Multi-UAV Mission Planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and María D R-Moreno and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaHybrid.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference},
pages = {1205--1208},
organization = {ACM},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Ramírez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Performance Evaluation of Multi-UAV Cooperative Mission Planning Models Inproceedings
In: Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II, pp. 203–212, 2015.
@inproceedings{DBLP:conf/iccci/Ramirez-Atencia15,
title = {Performance Evaluation of Multi-UAV Cooperative Mission Planning Models},
author = {Cristian Ramírez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
url = {http://dx.doi.org/10.1007/978-3-319-24306-1_20
http://aida.etsisi.upm.es/wp-content/uploads/2015/09/ramirez-atenciaPerformance.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015, Proceedings, Part II},
pages = {203--212},
crossref = {DBLP:conf/iccci/2015-2},
abstract = {The Multi-UAV Cooperative Mission Planning Problem (MCMPP) is a complex problem which can be represented with a lower or higher level of complexity. In this paper we present a MCMPP which is modelled as a Constraint Satisfaction Problem (CSP) with 5 increasing levels of complexity. Each level adds additional variables and constraints to the problem. Using previous models, we solve the problem using a Branch and Bound search designed to minimize the fuel consumption and number of UAVs employed in the mission, and the results show how runtime increases as the level of complexity increases in most cases, as expected, but there are some cases where the opposite happens.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodriguez-Fernández, Victor; Ramirez-Atencia, Cristian; Camacho, David
A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game Inproceedings
In: 2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015), 2015.
@inproceedings{rodriguez2015summary,
title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game},
author = {Victor Rodriguez-Fernández and Cristian Ramirez-Atencia and David Camacho},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {2nd Congreso de la Sociedad Espa~nola para las Ciencias del Videojuego (CoSeCiVi 2015)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rodriguez-Fernandez, Victor; Ramirez-Atencia, Cristian; Camacho, David
A multi-UAV Mission Planning videogame-based framework for player analysis Inproceedings
In: Evolutionary Computation (CEC), 2015 IEEE Congress on, pp. 1490–1497, IEEE 2015.
@inproceedings{rodriguez2015multi,
title = {A multi-UAV Mission Planning videogame-based framework for player analysis},
author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/07257064.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on},
pages = {1490--1497},
organization = {IEEE},
abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Palero, Fernando; Gonzalez-Pardo, Antonio; Camacho, David
Simple gamer interaction analysis through tower defense games Conference
6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014), Lecture Notes in Artificial Intelligence of Springer-Verlag, 2015.
@conference{2015-PaleroEtAl,
title = {Simple gamer interaction analysis through tower defense games},
author = {Fernando Palero and Antonio Gonzalez-Pardo and David Camacho},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014)},
pages = {185-194},
publisher = {Lecture Notes in Artificial Intelligence of Springer-Verlag},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Rodríguez-Fernández, Víctor; Menéndez, Héctor D; Camacho, David
User Profile Analysis for UAV Operators in a Simulation Environment Inproceedings
In: Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I, pp. 338–347, 2015.
@inproceedings{DBLP:conf/iccci/Rodriguez-Fernandez15,
title = {User Profile Analysis for UAV Operators in a Simulation Environment},
author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2015/09/iccci2015.pdfhttp://dx.doi.org/10.1007/978-3-319-24069-5_32},
year = {2015},
date = {2015-01-01},
booktitle = {Computational Collective Intelligence - 7th International Conference, ICCCI 2015, Madrid, Spain, September 21-23, 2015. Proceedings, Part I},
pages = {338--347},
crossref = {DBLP:conf/iccci/2015-1},
abstract = {Unmanned Aerial Vehicles have been a growing field of study over the last few years. The use of unmanned systems require a strong human supervision of one or many human operators, responsible for monitoring the mission status and avoiding possible incidents that might alter the execution and success of the operation. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process to deal with it. This work aims to present an evaluation methodology for inexperienced users. A multi-UAV simulation environment is used to carry out an experiment focused on the extraction of performance profiles, which can be used to evaluate the behavior and learning process of the users. A set of performance metrics is designed to define the profile of a user, and those profiles are discriminated using clustering algorithms. The results are analyzed to extract behavioral patterns that distinguish the users in the experiment, allowing the identification and selection of potential expert operators.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Palero, Fernando; Ramirez-Atencia, Cristian; Camacho, David
Online gamers classification using k-means Incollection
In: Intelligent Distributed Computing VIII, pp. 201–208, Springer, Cham, 2015.
@incollection{palero2015online,
title = {Online gamers classification using k-means},
author = {Fernando Palero and Cristian Ramirez-Atencia and David Camacho},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Intelligent Distributed Computing VIII},
pages = {201--208},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms Inproceedings
In: Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014), 2014.
@inproceedings{ramirez2014solving,
title = {Solving UAV Mission Planning based on Temporal Constaint Satisfaction Problem using Genetic Algorithms},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-09-12},
urldate = {2014-09-12},
booktitle = {Doctoral Program Proceedings of The 20th International Conference on Principles and Practice of Constraint Programming (CP 2014)},
abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) consists of a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. We study how this problem can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). This problem contains several constraints assuring UAVs are assigned to tasks they have enough characteristics to perform, and soft-constraints for optimizing the time and fuel spent in the process. Our goal is to implement this model and then try to solve it using Genetic Algorithms (GAs). For this purpose, we will carry out a mission simulation containing m UAVs with different sensors and characteristics located in different waypoints, and n requested tasks varying mission priorities. The GA will match the model constraints and use a multi-objective function in order to minimize the cost.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gonzalez-Pardo, Antonio; Camacho, David
Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms Conference
Ninth International Conference on Swarm Intelligence (ANTS 2014)., vol. 8667, Lecture Notes in Computer Science of Springer-Verlag, 2014.
@conference{2014-GonzalezCamachoANTS,
title = {Solving Resource-Constraint Project Scheduling Problems based on ACO algorithms},
author = {Antonio Gonzalez-Pardo and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2014/09/2014-ANTS-GonzalezCamacho.pdf},
year = {2014},
date = {2014-09-10},
urldate = {2014-09-10},
booktitle = {Ninth International Conference on Swarm Intelligence (ANTS 2014).},
volume = {8667},
pages = {290-291},
publisher = {Lecture Notes in Computer Science of Springer-Verlag},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gonzalez-Pardo, Antonio; Camacho, David
A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem Conference
2014 IEEE Conference on Evolutionary Computation (CEC 2014), 2014.
@conference{2014-GonzalezCamachoCEC,
title = {A New CSP Graph-Based Representation to Resource-Constrained Project Scheduling Problem},
author = {Antonio Gonzalez-Pardo and David Camacho},
year = {2014},
date = {2014-07-07},
urldate = {2014-07-07},
booktitle = {2014 IEEE Conference on Evolutionary Computation (CEC 2014)},
pages = {344-351},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David
Micro and Macro Lemmings simulations based on ants colonies Conference
EvoApp 2014, vol. In press, 2014.
@conference{14-GonzalezEtAl-EvoApp,
title = {Micro and Macro Lemmings simulations based on ants colonies},
author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho},
year = {2014},
date = {2014-04-23},
urldate = {2014-04-23},
booktitle = {EvoApp 2014},
volume = {In press},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gonzalez-Pardo, Antonio; Palero, Fernando; Camacho, David
An empirical study on collective intelligence algorithms for video games problem-solving Journal Article
In: Computing and Informatics, vol. In press, 2014, ISSN: 1335-9150.
@article{14-GonzalezEtAl-CAI,
title = {An empirical study on collective intelligence algorithms for video games problem-solving},
author = {Antonio Gonzalez-Pardo and Fernando Palero and David Camacho},
issn = {1335-9150},
year = {2014},
date = {2014-01-21},
urldate = {2014-01-21},
journal = {Computing and Informatics},
volume = {In press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
A simple CSP-based model for unmanned air vehicle mission planning Inproceedings
In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp. 146–153, IEEE 2014.
@inproceedings{ramirez2014simple,
title = {A simple CSP-based model for unmanned air vehicle mission planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings},
pages = {146--153},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian; Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David
Branching to find feasible solutions in unmanned air vehicle mission planning Inproceedings
In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 286–294, Springer, Cham 2014.
@inproceedings{ramirez2014branching,
title = {Branching to find feasible solutions in unmanned air vehicle mission planning},
author = {Cristian Ramirez-Atencia and Gema Bello-Orgaz and Maria D R-Moreno and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {International Conference on Intelligent Data Engineering and Automated Learning},
pages = {286--294},
organization = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ramirez-Atencia, Cristian
Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems Masters Thesis
Universidad Autónoma de Madrid, 2014.
@mastersthesis{ramirez2014modelling,
title = {Modelling Unmanned Vehicles Mission Planning problems as Constraint Satisfaction Problems},
author = {Cristian Ramirez-Atencia },
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
school = {Universidad Autónoma de Madrid},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
Menendez, Hector D; Barrero, David F; Camacho, David
A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm Inproceedings
In: Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 2724–2731, IEEE 2014.
@inproceedings{menendez2014co,
title = {A Co-Evolutionary Multi-Objective approach for a K-adaptive graph-based clustering algorithm},
author = {Hector D Menendez and David F Barrero and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Evolutionary Computation (CEC), 2014 IEEE Congress on},
pages = {2724--2731},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Menendez, Hector D; Camacho, David
A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation Inproceedings
In: Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on, pp. 234–241, IEEE 2014.
@inproceedings{menendez2014multi,
title = {A Multi-Objective Graph-based Genetic Algorithm for Image Segmentation},
author = {Hector D Menendez and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Innovations in Intelligent Systems and Applications (INISTA) Proceedings, 2014 IEEE International Symposium on},
pages = {234--241},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gonzalez-Pardo, Antonio; Rosa, Angeles; Camacho, David
Behaviour-based identification of student communities in Virtual Worlds Journal Article
In: Computer Science and Information Systems (COMSIS), vol. 11, no. 1, pp. 195-213, 2014, ISSN: 1820-0214.
@article{2013-GonzalezEtAl-ComSIS,
title = {Behaviour-based identification of student communities in Virtual Worlds},
author = {Antonio Gonzalez-Pardo and Angeles Rosa and David Camacho},
url = {http://aida.etsisi.upm.es/wp-content/uploads/2014/02/2014-COMSIS-GonzalezEtAl.pdf},
issn = {1820-0214},
year = {2014},
date = {2014-01-01},
journal = {Computer Science and Information Systems (COMSIS)},
volume = {11},
number = {1},
pages = {195-213},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bello-Orgaz, Gema; Menéndez, Héctor D; Okazaki, Shintaro; Camacho, David
Combining Social-Based Data Mining Techniques To Extract Collective Trends From Twitter Journal Article
In: Malaysian Journal of Computer Science, vol. 27, no. 2, pp. 95–111, 2014.
@article{bello2014combiningb,
title = {Combining Social-Based Data Mining Techniques To Extract Collective Trends From Twitter},
author = {Gema Bello-Orgaz and Héctor D Menéndez and Shintaro Okazaki and David Camacho},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {Malaysian Journal of Computer Science},
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number = {2},
pages = {95--111},
publisher = {Electronic Journal of University of Malaya},
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Menéndez, Héctor D; Barrero, David F; Camacho, David
A Genetic Graph-based Approach for Partitional Clustering Journal Article
In: International journal of neural systems, vol. 24, no. 03, 2014.
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title = {A Genetic Graph-based Approach for Partitional Clustering},
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Menéndez, Héctor D; Plaza, Laura; Camacho, David
Combining graph connectivity and genetic clustering to improve biomedical summarization Inproceedings
In: Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 2740–2747, IEEE 2014.
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Bello-Orgaz, Gema; Menéndez, Héctor D; Okazaki, Shintaro; Camacho, David
Combining Social-Based Data Mining Techniques To Extract Collective Trends From Twitter Journal Article
In: Malaysian Journal of Computer Science, vol. 27, no. 2, 2014, ISBN: 0127-9084.
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Menéndez, Héctor D; Vindel, Rafael; Camacho, David
Combining time series and clustering to extract gamer profile evolution Incollection
In: Computational Collective Intelligence. Technologies and Applications, pp. 262–271, Springer International Publishing, 2014.
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Bello-Orgaz, Gema; Camacho, David
Evolutionary clustering algorithm for community detection using graph-based information Inproceedings
In: Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 930–937, IEEE 2014.
@inproceedings{bello2014evolutionary,
title = {Evolutionary clustering algorithm for community detection using graph-based information},
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Menéndez, Héctor D; Otero, Fernando EB; Camacho, David
MACOC: a medoid-based ACO clustering algorithm Incollection
In: Swarm Intelligence, pp. 122–133, Springer International Publishing, 2014.
@incollection{menendez2014macoc,
title = {MACOC: a medoid-based ACO clustering algorithm},
author = {Héctor D Menéndez and Fernando EB Otero and David Camacho},
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Menéndez, Héctor D; Vázquez, Miguel; Camacho, David
Mixed Clustering Methods to Forecast Baseball Trends Incollection
In: Intelligent Distributed Computing VIII, pp. 175–184, Springer International Publishing, 2014.
@incollection{menendez2014mixed,
title = {Mixed Clustering Methods to Forecast Baseball Trends},
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Menéndez, Héctor D; Otero, Fernando EB; Camacho, David
SACOC: A spectral-based ACO clustering algorithm Incollection
In: Intelligent Distributed Computing VIII, pp. 185–194, Springer International Publishing, 2014.
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Gonzalez-Pardo, Antonio; Camacho, David
Environmental influence in bio-inspired game level solver algorithms Conference
7th International Symposium on Intelligent Distributed Computing (IDC 2013)., vol. 511, Studies in Computational Intelligence Springer Berlin Heidelberg, 2014, ISBN: 978-3-319-01570-5.
@conference{2013-IDC-GonzalezCamacho,
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Gonzalez-Pardo, Antonio; Cajias, Raul; Camacho, David
An Agent-Based Simulation of Christakis-Fowler Social Model Conference
Proceedings of the 5th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2013), vol. 513, Lecture Notes in Artificial Intelligence of Springer-Verlag Springer Verlag, 2013, ISBN: 978-3-319-01786-0.
@conference{13-GonzalezEtAl-ICCCI,
title = {An Agent-Based Simulation of Christakis-Fowler Social Model},
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Bello-Orgaz, Gema; Menéndez, Héctor D; Okazaki, Shintaro; Camacho, David
Extracting Collective Trends from Twitter Using Social-Based Data Mining Incollection
In: 5th International Conference on Computational Collective Intelligence (ICCCI 2013), pp. 622–630, Springer-Verlag, 2013.
@incollection{bello2013extracting,
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Bello-Orgaz, Gema; Barrero, David F; R-Moreno, Maria D; Camacho, David
Acquisition of Business Intelligence from Human Experience in Route Planning Journal Article
In: Enterprise Information Systems, no. Impact Factor: 9.26-Q1, 2013, ISSN: 1751-7575.
@article{Bello-Orgaz:2012:EIS,
title = {Acquisition of Business Intelligence from Human Experience in Route Planning},
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Barrero, David F; Menéndez, Héctor D; Camacho, David
A Multi-Objective Genetic Graph-based Clustering Algorithm with Memory Optimization Conference
IEEE Congress on Evolutionary Computation (CEC 2013), 2013.
@conference{Hector-2013-CEC,
title = {A Multi-Objective Genetic Graph-based Clustering Algorithm with Memory Optimization},
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Plaza, Laura; Menéndez, Héctor D; Camacho, David
A Genetic Graph-based Clustering approach to Biomedical Summarization Conference
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, no. 10, WIMS '13 ACM Press, Madrid, Spain, 2013, ISBN: 978-1-4503-1850-1/13/06.
@conference{Menendez:2013:WIMS,
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Bello-Orgaz, Gema; Camacho, David
Comparative study of text clustering techniques in virtual worlds Conference
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS '13 ACM Press, Madrid, Spain, 2013, ISBN: 978-1-4503-1850-1/13/06.
@conference{Bello-Orgaz:2013:CST:2479787.2479818,
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Gonzalez-Pardo, Antonio; Camacho, David
A new CSP graph-based representation for Ant Colony Optimization Conference
2013 IEEE Conference on Evolutionary Computation (CEC 2013), vol. 1, 2013.
@conference{13-GonzalezCamacho-CEC,
title = {A new CSP graph-based representation for Ant Colony Optimization},
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Gonzalez-Pardo, Antonio; Camacho, David
Environmental influence in bio-inspired game level solver algorithms Conference
Proceedings of the 7th International Symposium on Intelligent Distributed Computing - IDC 2013, Studies in Computational Intelligence Springer Berlin Heidelberg, 2013.
@conference{2013-GonzalezCamacho-IDC,
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Menendez, Hector D; Bello-Orgaz, Gema; Camacho, David
Extracting Behavioural Models from 2010 FIFA World Cup Journal Article
In: Journal of Systems Science and Complexity, vol. 26, no. 1, pp. 43-61, 2013, ISSN: 1009-6124.
@article{Menendez:2013:JSSC,
title = {Extracting Behavioural Models from 2010 FIFA World Cup},
author = {Hector D Menendez and Gema Bello-Orgaz and David Camacho},
url = {http://link.springer.com/article/10.1007%2Fs11424-013-2289-9},
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abstract = {The FIFA World Cup™ is the most profitable worldwide event. The FIFA publishes global statistics of this competition which provide global data about the players and teams during the competition. This work is focused on the extraction of behavioural patterns for both, players and teams strategies, through the automated analysis of this dataset. The knowledge and models extracted in this work could be applied to soccer leagues or even it could be oriented to sport betting. However, the main contribution is related to the study on several automatic knowledge extraction techniques, such as clustering methods, and how these techniques can be used to obtain useful behavioural models from a global statistics dataset. The information provided by the clustering algorithms shows similar properties which have been combined to define the models, making the human interpretation of these statistics easier. Finally, the most successful teams strategies have been analysed and compared.},
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Barrero, David F; R-Moreno, Maria D; Camacho, David
Improving experimental methods on success rates in Evolutionary Computation Journal Article
In: Journal of Experimental & Theoretical Artificial Intelligence, 2013, ISSN: 1362-3079.
@article{Barrero2013,
title = {Improving experimental methods on success rates in Evolutionary Computation},
author = {David F Barrero and Maria D R-Moreno and David Camacho},
issn = {1362-3079},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {Journal of Experimental & Theoretical Artificial Intelligence},
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Camacho, David; Granados, Ana; Rodríguez, Francisco Borja
Is the contextual information relevant in text clustering by compression? Journal Article
In: Expert Systems with Applications, vol. 39, no. 10, pp. 8537 - 8546, 2012, ISSN: 0957-4174.
@article{Granados20128537,
title = {Is the contextual information relevant in text clustering by compression?},
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url = {http://dx.doi.org/10.1016/j.eswa.2012.01.215},
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Gonzalez-Pardo, Antonio; Camacho, David
Maximal Component detection in graphs using swarm-based and genetic algorithms Conference
Proceedings of the 6th International Symposium on Intelligent Distributed Computing - IDC 2012, vol. 446, Studies in Computational Intelligence Springer Berlin Heidelberg, 2012, ISBN: 978-3-642-32523-6.
@conference{2012-GonzalezCamacho,
title = {Maximal Component detection in graphs using swarm-based and genetic algorithms},
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booktitle = {Proceedings of the 6th International Symposium on Intelligent Distributed Computing - IDC 2012},
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Bello-Orgaz, Gema; Menendez, Hector D; Camacho, David
Adaptive K-Means Algorithm for overlapped graph clustering Journal Article
In: International Journal of Neural Systems, vol. 22 (Impact Factor:5.1 -Q1), no. 05, pp. 1250018 1–19, 2012, ISSN: 0129-0657.
@article{Bello-Orgaz:2012:IJNS,
title = {Adaptive K-Means Algorithm for overlapped graph clustering},
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url = {http://www.worldscientific.com/doi/abs/10.1142/S0129065712500189
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Gonzalez-Pardo, Antonio; Varona, Pablo; Camacho, David; Rodríguez, Francisco Borja
Communication by identity in bio-inspired multi-agent systems Journal Article
In: International Journal Concurrency and Computation: Practice & Experience., vol. 2012, no. 24, pp. 589-603, 2012, ISSN: 1532-0626.
@article{12-Gonzalez-Pardo-CCPE,
title = {Communication by identity in bio-inspired multi-agent systems},
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Menéndez, Héctor D; Camacho, David
A Genetic Graph-Based Clustering Algorithm Incollection
In: Intelligent Data Engineering and Automated Learning - IDEAL 2012, vol. 7435, pp. 216-225, Springer Berlin / Heidelberg, 2012, ISSN: 978-3-642-32638-7, (10.1007/978-3-642-32639-4_27).
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title = {A Genetic Graph-Based Clustering Algorithm},
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abstract = {The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.},
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Bello-Orgaz, Gema; R-Moreno, Maria D; Camacho, David; Barrero, David F
Clustering avatars behaviours from virtual worlds interactions Inproceedings
In: Proceedings of the 4th International Workshop on Web Intelligence & Communities, pp. 4:1–4:7, ACM, New York, NY, USA, 2012, ISSN: 978-1-4503-1189-2.
@inproceedings{Orgaz:2012:CAB:2189736.2189743,
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Menéndez, Héctor D; Bello-Orgaz, Gema; Camacho, David
Features selection from high-dimensional web data using clustering analysis Inproceedings
In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, pp. 20:1–20:9, ACM, New York, NY, USA, 2012, ISSN: 978-1-4503-0915-8.
@inproceedings{Menendez:2012:FSH:2254129.2254155,
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Cajias, Raul; Gonzalez-Pardo, Antonio; Camacho, David
A Multi-Agent Simulation Platform Applied to the Study of Urban Traffic Lights Conference
Proceedings of the 6th International Conference on Software and Data Technologies, vol. 1, 2011, ISBN: 978-989-8425-76-8.
@conference{11-Cajias-ICSOFT,
title = {A Multi-Agent Simulation Platform Applied to the Study of Urban Traffic Lights},
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Camacho, David; Granados, Ana; Cebrián, Manuel; Rodríguez, Francisco Borja
Reducing the Loss of Information through Annealing Text Distortion Journal Article
In: IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 7, pp. 1090-1102, 2011, ISSN: 1041-4347.
@article{5582094,
title = {Reducing the Loss of Information through Annealing Text Distortion},
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Cajias, Raul; González-Pardo, Antonio; Camacho, David
A Swarm Simulation Platform For Agent-Based Social Simulations Conference
5th International Symposium on Intelligent Distributed Computing (IDC 2011), vol. 382, Springer Berlin / Heidelberg, 2011, ISBN: 978-3-642-24012-6.
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title = {A Swarm Simulation Platform For Agent-Based Social Simulations},
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Jiménez-Díaz, Guillermo; Menéndez, Héctor D; Camacho, David; González-Calero, Pedro A
Predicting Performance in Team Games Conference
ICAART 2011 - Proceedings of the 3ed International Conference on Agents and Artificial Intelligence, vol. Vol 1, INSTICC Institude - for systems and Control Technologies of Information and Communication, 2011, ISBN: 978-989-8425-40-3.
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Cajias, Raul; Gonzalez-Pardo, Antonio; Camacho, David
A Multi-Agent Traffic Simulation Framework for Evaluating the Impact of Traffic Lights Conference
Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART), vol. 2, Communications in Computer and Information Science Springer Berlin / Heidelberg, 2011, ISBN: 978-989-8425-41-6.
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Bello-Orgaz, Gema; Cajias, Raul; Camacho, David
Study on the Impact of Crowd-Based Voting Schemes in the ’Eurovision’ European Contest Inproceedings
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Bello-Orgaz, Gema; Menéndez, Héctor D; Camacho, David
Using the Clustering Coefficient to Guide a Genetic-Based Communities Finding Algorithm Incollection
In: Intelligent Data Engineering and Automated Learning - IDEAL 2011, vol. 6936, pp. 160-169, Springer Berlin / Heidelberg, 2011, ISSN: 978-3-642-23877-2.
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Gonzalez-Pardo, Antonio; Rodríguez, Francisco Borja; Pulido, Estrella; Camacho, David
Using Virtual Worlds for Behaviour Clustering-based Analysis Conference
ACM Workshop on Surreal Media and Virtual Cloning, SMVC '10 ACM, ACM New York, NY, USA, 2010, ISSN: 978-1-60558-933-6.
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Optimal message interchange in a self-organizing multi-agent system Conference
Intelligent Distributed Computing IV, vol. 315, Studies in Computational Intelligence IV Springer Verlag Berlin Heidelberg, 2010, ISSN: 978-3-642-15210-8.
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Influence of music representation on compression-based clustering Conference
IEEE World Congress on Computational Intelligence, IEEE Xplore, 2010, ISSN: 978-1-4244-6910-9.
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Barrero, David F; Gonzalez-Pardo, Antonio; Camacho, David; R-Moreno, Maria D
Distributed Parameter Tunning for Genetic Algorithm Journal Article
In: Computer Science and Information Systems (COMSIS), vol. 7, no. 3, pp. 661-677, 2010, ISSN: 1820-0214.
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Granados, Ana; Martínez, Rafael; Camacho, David; Rodríguez, Francisco Borja
Relevance of Contextual Information in Compression-Based Text Clustering Inproceedings
In: IDEAL, pp. 259-266, 2010.
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R-Moreno, Maria D; Camacho, David; Barrero, David F; Gutierrez, Miguel
A Decision Support System for Logistics Operations Inproceedings
In: SOCO, pp. 103-110, 2010.
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Barrero, David F; Camacho, David; R-Moreno, Maria D
A Framework for Agent-Based Evaluation of Genetic Algorithms Inproceedings
In: IDC, pp. 31-41, 2009.
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Aler, Ricardo; Valls, José María; Camacho, David; López, Alberto
Programming Robosoccer agents by modeling human behavior Journal Article
In: Expert Syst. Appl., vol. 36, no. 2, pp. 1850-1859, 2009.
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R-Moreno, Maria D; Camacho, David; Obieta, Unai
A plan-based tool for automatic eLearning courses redesign Journal Article
In: IJCSA, vol. 5, no. 1, pp. 33-48, 2008.
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Granados, Ana; Cebrián, Manuel; Camacho, David; Rodríguez, Francisco Borja
Evaluating the Impact of Information Distortion on Normalized Compression Distance-driven Text Clustering Journal Article
In: CoRR, vol. abs/0711.4075, 2007.
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Camacho, David; Aler, Ricardo; Borrajo, Daniel; Molina, José M
Multi-agent plan based information gathering Journal Article
In: Appl. Intell., vol. 25, no. 1, pp. 59-71, 2006.
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Camacho, David; Fernández, Fernando; Rodelgo, Miguel A
Roboskeleton: An architecture for coordinating robot soccer agents Journal Article
In: Eng. Appl. of AI, vol. 19, no. 2, pp. 179-188, 2006.
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Camacho, David; Aler, Ricardo; Borrajo, Daniel; Molina, José M
A Multi-Agent architecture for intelligent gathering systems Journal Article
In: AI Commun., vol. 18, no. 1, pp. 15-32, 2005.
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Camacho, David; Aler, Ricardo
Software and Performance Measures for Evaluating Multi-Agent Frameworks Journal Article
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Camacho, David; Aler, Ricardo; Cuadrado, Juan
Intelligent Agents for Data Mining and Information Retrieval Book Chapter
In: Chapter Rule-Based Parsing for Web Data Extracti, pp. 65-87. Chapter 5, Idea Group Publishing, 2004.
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Camacho, David; López, Miguel A; Aler, Ricardo
Desining Flexible Software Components for Automatic Information Extraction in Web Agents Inproceedings
In: International Conference on Intelligent Agents, Web Technology and Internet Commerce, Vienna, Austria, 2003.
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Camacho, David
Coordination of Planning Agents to Solve Problems in the Web Journal Article
In: AI Communications, The European Journal on Artificial Intelligence. Ed. by IOS Press., vol. 16, no. 4, pp. 309-311, 2003.
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Camacho, David; López, Miguel A; Aler, Ricardo
Semi-Automatic Parsing for Web Knowledge Extraction Inproceedings
In: Applied Informatics, pp. 303-308, 2003.
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Thesis: Coordination of planning agents to solve problems in the Web Journal Article
In: AI Commun., vol. 16, no. 4, pp. 309-311, 2003.
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Aler, Ricardo; Camacho, David; Moscardini, Alfredo
Cooperation Between Agents to Evolve Complete Programs Book Chapter
In: Chapter In Intelligent Agent Software Engineerin, pp. 213–228, Valentina Plekhanova. University of Sunderland, United Kingdom. Ed. by Idea Group Publishing, 2003.
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Camacho, David; López, Miguel A; Aler, Ricardo
Designing Flexible Software Components for Automatic Information Extraction in Web Agents Inproceedings
In: International Conference on Intelligent Agents, Web Technology and Internet Commerce, Vienna, Austria, 2003.
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Camacho, David; Hernando, Javier
A Preliminary Study of Jade and JATLite Frameworks in Web Domains Inproceedings
In: Internet, e-com and Artificial Intelligence Ist International Workshop on Practical Applications of Agents and Multiagent Systems (IWPAAMS'2002), pp. 113–122, Salamanca, Spain, 2002.
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Camacho, David; Aler, Ricardo; Castro, César; Molina, José M
Analysis of Internet Multi-Agent Based System for Zeus and SkeletonAgent Frameworks Inproceedings
In: Second WSEAS International Conference on Multimedia, Internet and Video Technologies (ICOMIV 2002), IEEE Skiathos, Greece, 2002.
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Performance Evaluation of Zeus, Jade and SkeletonAgent Frameworks Inproceedings
In: Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2002), IEEE Hammamet, Tunisia, 2002.
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Camacho, David; Molina, José M; Borrajo, Daniel; Aler, Ricardo
Solving Travel Problems by Integrating WEB Information with Planning Inproceedings
In: ISMIS, pp. 482-490, 2002.
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Camacho, David; Molina, José M; Borrajo, Daniel; Aler, Ricardo
MAPWEB: Cooperation between Planning Agents and Web Agents Journal Article
In: Information & Security: An International Journal. Special issue on Agent-based Technologies, vol. 8, no. 2, pp. 209–238, 2002.
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Camacho, David
Coordinación de Agentes de Planificación para la Resolución de Problemas en Web PhD Thesis
Departamento de Informática. Universidad Carlos III de Madrid, 2001.
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Camacho, David; Hernández, César; Molina, José M
Information Classification using Fuzzy Knowledge Based Agents Inproceedings
In: Proceedings of the IEEE Systems, Man, and Cybernectics Conference (SMC-2001), IEEE USA, 2001.
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Camacho, David; Cuadrado, Juan; Borrajo, Daniel; Molina, José M
Desarrollo de Agentes de Usuario para el Acceso Distribuido a Información en Web Inproceedings
In: Turismo y Tecnologías de la Información y las Comunicaciones, pp. 111–134, Madrid, Spain, 2001.
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Camacho, David; Borrajo, Daniel; Molina, José M
Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain Journal Article
In: Autonomous Agents and Multi-Agent Systems, vol. 4, no. 4, pp. 387-392, 2001.
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A Multiagent Approach for Electronic Travel Planning Inproceedings
In: Proceedings of the Second International Bi-Conference Workshop on Agent-Oriented Information Systems (AOIS-2000), AAAI Austin, TX (USA), 2000.
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Camacho, David; Borrajo, Daniel; Molina, José M
Electronic Tourism in the Web. An Artificial Intelligence Approach Inproceedings
In: Turismo y Teconologías de la Información y las Comunicaciones, pp. 307-326, Madrid, Spain, 2000.
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Camacho, David; Borrajo, Daniel; Molina, José M
TravelPlan: A MultiAgent System to Solve Web Electronic Travel Problems Inproceedings
In: Workshop on Agent-Based Recommender Systems. Fourth International Conference on Autonomous Agents, ACM Barcelona, Spain, 2000.
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Camacho, David; Hernández, César; Molina, José M
Information Classification in Web Agents using Fuzzy Knowledge for Distance Evaluation Inproceedings
In: WSES International Conference on: Fuzzy Sets & Fuzzy Systems (FSFS-01), IEEE Puerto De La Cruz (Tenerife), Spain, 2000.
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Camacho, David; Molina, José M; Borrajo, Daniel
Planificación en Internet basada en una Arquitectura Multiagente Inproceedings
In: II Jornadas de Ingeniería Telemática (JITEL99), pp. 289-296, Leganes, Madrid(Spain), 1999.
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Borrajo, Daniel; Camacho, David; Silva, Andres
Multistrategy Relational Learning of Heuristics for Problem Solving Inproceedings
In: Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence, Cambridge, UK, 1999.
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