@article{Guedan2024Airport,

title = {Airport take-off and landing optimization through genetic algorithms},

author = {Fernando Guedan-Pecker and Cristian Ramirez-Atencia},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13565},

doi = {https://doi.org/10.1111/exsy.13565},

issn = {0266-4720},

year  = {2024},

date = {2024-02-20},

urldate = {2024-02-20},

journal = {Expert Systems},

volume = {41},

number = {13565},

issue = {8},

pages = {1-30},

abstract = {This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take-off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine-tune the optimization process. This dual-focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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},

urldate = {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 = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{nokey,

title = {Wind power ramp event detection with a hybrid neuro-evolutionary approach},

author = {Laura Cornejo-Bueno and C. Camacho-Gómez and Adrián Aybar-Ruíz and Luis Prieto and Alberto Barea-Ropero and Sancho Salcedo-Sanz},

doi = {s00521-018-3707-7},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Neural Computing and Applications},

volume = {32},

number = {2},

pages = {391-402},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@article{nokey,

title = {Optimal microgrid topology design and siting of distributed generation sources using a multi-objective substrate layer Coral Reefs Optimization algorithm},

author = {Silvia Jiménez-Fernández and C. Camacho-Gómez and Ricardo Mallol-Poyato and Juan Carlos Fernández and Javier Del Ser and Antonio Portilla-Figueras and Sancho Salcedo-Sanz},

doi = {10.3390/su11010169},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Sustainability},

volume = {11},

number = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{menendez2014genetic,

title = {A Genetic Graph-based Approach for Partitional Clustering},

author = {Héctor D Menéndez and David F Barrero and David Camacho},

year  = {2014},

date = {2014-01-01},

journal = {International journal of neural systems},

volume = {24},

number = {03},

publisher = {World Scientific Publishing Company},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{bello2014combining,

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},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2014/12/MJCS13_BelloOrgaz-Menendez.pdf},

isbn = {0127-9084},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Malaysian Journal of Computer Science},

volume = {27},

number = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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},

volume = {27},

number = {2},

pages = {95--111},

publisher = {Electronic Journal of University of Malaya},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bello-Orgaz:2012:EIS,

title = {Acquisition of Business Intelligence from Human Experience in Route Planning},

author = {Gema Bello-Orgaz and David F Barrero and Maria D R-Moreno and David Camacho},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2012/12/eis.pdf},

issn = {1751-7575},

year  = {2013},

date = {2013-06-22},

urldate = {2013-06-22},

journal = {Enterprise Information Systems},

number = {Impact Factor: 9.26-Q1},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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},

issn = {1009-6124},

year  = {2013},

date = {2013-02-01},

urldate = {2013-02-01},

journal = {Journal of Systems Science and Complexity},

volume = {26},

number = {1},

pages = {43-61},

publisher = {Academy of Mathematics and Chinese Academy Sciences of Systems Science},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Granados20128537,

title = {Is the contextual information relevant in text clustering by compression?},

author = {David Camacho and Ana Granados and Francisco Borja Rodrí­guez},

url = {http://dx.doi.org/10.1016/j.eswa.2012.01.215},

issn = {0957-4174},

year  = {2012},

date = {2012-08-01},

urldate = {2012-08-01},

journal = {Expert Systems with Applications},

volume = {39},

number = {10},

pages = {8537 - 8546},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bello-Orgaz:2012:IJNS,

title = {Adaptive K-Means Algorithm for overlapped graph clustering},

author = {Gema Bello-Orgaz and Hector D Menendez and David Camacho},

url = {http://www.worldscientific.com/doi/abs/10.1142/S0129065712500189 

http://aida.etsisi.upm.es/wp-content/uploads/2012/09/ijns-2012.pdf},

issn = {0129-0657},

year  = {2012},

date = {2012-06-18},

urldate = {2012-06-18},

journal = {International Journal of Neural Systems},

volume = {22 (Impact Factor:5.1 -Q1)},

number = {05},

pages = {1250018 1--19},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{12-Gonzalez-Pardo-CCPE,

title = {Communication by identity in bio-inspired multi-agent systems},

author = {Antonio Gonzalez-Pardo and Pablo Varona and David Camacho and Francisco Borja Rodrí­guez},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2012/03/CCPE-GonzalezPardoEtAl.pdf},

issn = {1532-0626},

year  = {2012},

date = {2012-03-03},

urldate = {2012-03-03},

journal = {International Journal Concurrency and Computation: Practice & Experience.},

volume = {2012},

number = {24},

pages = {589-603},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{5582094,

title = {Reducing the Loss of Information through Annealing Text Distortion},

author = {David Camacho and Ana Granados and Manuel Cebrián and Francisco Borja Rodrí­guez},

url = {http://dx.doi.org/10.1109/TKDE.2010.173},

issn = {1041-4347},

year  = {2011},

date = {2011-07-01},

urldate = {2011-07-01},

journal = {IEEE Transactions on Knowledge and Data Engineering},

volume = {23},

number = {7},

pages = {1090-1102},

publisher = {IEEE Press},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10-BarreroEtAl-COMSIS,

title = {Distributed Parameter Tunning for Genetic Algorithm},

author = {David F Barrero and Antonio Gonzalez-Pardo and David Camacho and Maria D R-Moreno},

url = {http://www.comsis.org/ComSIS/Vol7No3/RegularPapers/paper13.pdf},

issn = {1820-0214},

year  = {2010},

date = {2010-06-25},

journal = {Computer Science and Information Systems (COMSIS)},

volume = {7},

number = {3},

pages = {661-677},

publisher = {Publisher ComSIS Consortium},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/eswa/AlerVCL09a,

title = {Programming Robosoccer agents by modeling human behavior},

author = {Ricardo Aler and José Marí­a Valls and David Camacho and Alberto López},

year  = {2009},

date = {2009-01-01},

journal = {Expert Syst. Appl.},

volume = {36},

number = {2},

pages = {1850-1859},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/ijcsa/Rodriguez-MorenoCO08,

title = {A plan-based tool for automatic eLearning courses redesign},

author = {Maria D R-Moreno and David Camacho and Unai Obieta},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

journal = {IJCSA},

volume = {5},

number = {1},

pages = {33-48},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-0711-4075,

title = {Evaluating the Impact of Information Distortion on Normalized Compression Distance-driven Text Clustering},

author = {Ana Granados and Manuel Cebrián and David Camacho and Francisco Borja Rodrí­guez},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

journal = {CoRR},

volume = {abs/0711.4075},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/apin/CamachoABM06,

title = {Multi-agent plan based information gathering},

author = {David Camacho and Ricardo Aler and Daniel Borrajo and José M Molina},

year  = {2006},

date = {2006-01-01},

journal = {Appl. Intell.},

volume = {25},

number = {1},

pages = {59-71},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/eaai/CamachoFR06,

title = {Roboskeleton: An architecture for coordinating robot soccer agents},

author = {David Camacho and Fernando Fernández and Miguel A Rodelgo},

url = {http://aida.etsisi.upm.es/wp-content/uploads/2011/06/aai-dcamacho.pdf},

year  = {2006},

date = {2006-01-01},

journal = {Eng. Appl. of AI},

volume = {19},

number = {2},

pages = {179-188},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/aicom/CamachoABM05,

title = {A Multi-Agent architecture for intelligent gathering systems},

author = {David Camacho and Ricardo Aler and Daniel Borrajo and José M Molina},

year  = {2005},

date = {2005-01-01},

journal = {AI Commun.},

volume = {18},

number = {1},

pages = {15-32},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/aai/CamachoA05,

title = {Software and Performance Measures for Evaluating Multi-Agent Frameworks},

author = {David Camacho and Ricardo Aler},

year  = {2005},

date = {2005-01-01},

journal = {Applied Artificial Intelligence},

volume = {19},

number = {6},

pages = {645-657},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{camacho:aicomm03,

title = {Coordination of Planning Agents to Solve Problems in the Web},

author = {David Camacho},

year  = {2003},

date = {2003-12-08},

journal = {AI Communications, The European Journal on Artificial Intelligence. Ed. by IOS Press.},

volume = {16},

number = {4},

pages = {309-311},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/aicom/Camacho03,

title = {Thesis: Coordination of planning agents to solve problems in the Web},

author = {David Camacho},

year  = {2003},

date = {2003-01-01},

journal = {AI Commun.},

volume = {16},

number = {4},

pages = {309-311},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{camacho:mapweb,

title = {MAPWEB: Cooperation between Planning Agents and Web Agents},

author = {David Camacho and José M Molina and Daniel Borrajo and Ricardo Aler},

year  = {2002},

date = {2002-01-01},

urldate = {2002-01-01},

journal = {Information & Security: An International Journal. Special issue on Agent-based Technologies},

volume = {8},

number = {2},

pages = {209--238},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/aamas/CamachoBM01,

title = {Intelligent Travel Planning: A MultiAgent Planning System to Solve Web Problems in the e-Tourism Domain},

author = {David Camacho and Daniel Borrajo and José M Molina},

year  = {2001},

date = {2001-01-01},

journal = {Autonomous Agents and Multi-Agent Systems},

volume = {4},

number = {4},

pages = {387-392},

keywords = {},

pubstate = {published},

tppubtype = {article}

}