@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 = {Airport operations, Constraint Satisfaction Problems, Genetic Algorithms, Planning, Pollution},

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 = {Conjunction Assessment, Deep Learning, Filtering, Machine Learning, Orbits, Space, Space Debris, Space Environment Management, Space Situational Awareness, Space Traffic Management, Time Series},

pubstate = {published},

tppubtype = {article}

}

@article{de2023epsilod,

title = {EPSILOD: efficient parallel skeleton for generic iterative stencil computations in distributed GPUs},

author = {Manuel Castro and Inmaculada Santamaria-Valenzuela and Yuri Torres and Arturo Gonzalez-Escribano and Diego R Llanos},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {The Journal of Supercomputing},

volume = {79},

number = {9},

pages = {9409–9442},

publisher = {Springer},

keywords = {Parallel Computing},

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 = {Deep Learning, Disinformation, Embeddings, Natural language inference, Natural Language Processing, Sentence alignment, Transformers},

pubstate = {published},

tppubtype = {article}

}

@article{domingueztesting,

title = {Testing the Performance, Adequacy, and Applicability of an Artificial Intelligent Model for Pediatric Pneumonia Diagnosis},

author = {Sara Domínguez-Rodríguez and Helena Liz López and Ángel Panizo-Lledot and Álvaro Ballesteros and Ron Dagan and David Greenberg and Lourdes Gutiérrez and Pablo Rojo and Enrique Otheo and Juan Carlos Galán and others},

year  = {2022},

date = {2022-05-27},

urldate = {2022-05-27},

keywords = {Health},

pubstate = {published},

tppubtype = {article}

}

@article{martin_facter-check_2022,

title = {FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference},

author = {Alejandro Martín and Javier Huertas-Tato and Álvaro Huertas-García and Guillermo Villar-Rodríguez and David Camacho},

url = {http://arxiv.org/abs/2110.14532},

year  = {2022},

date = {2022-02-01},

urldate = {2022-02-01},

journal = {arXiv:2110.14532 [cs]},

abstract = {Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidences about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking that can be used for either applications designed for the general public and by fact-checking organisations. FacTeR-Check enables retrieving fact-checked information, unchecked claims verification and tracking dangerous information over social media. This architectures involves several modules developed to evaluate semantic similarity, to calculate natural language inference and to retrieve information from Online Social Networks. The union of all these components builds a semi-automated fact-checking tool able of verifying new claims, to extract related evidence, and to track the evolution of a hoax on a OSN. While individual modules are validated on related benchmarks (mainly MSTS and SICK), the complete architecture is validated using a new dataset called NLI19-SP that is publicly released with COVID-19 related hoaxes and tweets from Spanish social media. Our results show state-of-the-art performance on the individual benchmarks, as well as producing a useful analysis of the evolution over time of 61 different hoaxes.},

note = {arXiv: 2110.14532},

keywords = {Disinformation, Natural Language Processing},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/grurint/ikab174,

title = {Clarifying Assumptions About Artificial Intelligence Before Revolutionising Patent Law},

author = {Daria Kim and Maximilian Alber and Man Wai Kwok and Jelena Mitrovic and Cristian Ramirez-Atencia and Jesús Alberto Rodríguez Pérez and Heiner Zille},

url = {https://doi.org/10.1093/grurint/ikab174},

doi = {10.1093/grurint/ikab174},

issn = {2632-8623},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {GRUR International},

abstract = {This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of ‘artificial ingenuity’. The objective is to draw a more realistic and nuanced picture of the human-computer interaction in solving technical problems than where ‘intelligent’ systems autonomously yield inventions. A detailed technical perspective is presented for each assumption, followed by a discussion of pertinent uncertainties for patent law. Overall, it is argued that implications of machine learning for the patent system in its core tenets appear far less revolutionary than is often posited.},

note = {ikab174},

keywords = {Artificial Intelligence, IT Law},

pubstate = {published},

tppubtype = {article}

}

@article{panizo2022youngres,

title = {YoungRes: A Serious Game-Based Intervention to Increase Youngsters Resilience Against Extremist Ideologies},

author = {Ángel Panizo-Lledot and Javier Torregrosa and Raquel Menéndez-Ferreira and Daniel López-Fernández and Pedro P Alarcón and David Camacho},

doi = {10.1109/ACCESS.2022.3157526},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {IEEE Access},

volume = {10},

pages = {28564--28578},

publisher = {IEEE},

keywords = {Emotional Intelligence, Extremism, Psychology, Serious Games, Videogames},

pubstate = {published},

tppubtype = {article}

}

@article{menendez2022design,

title = {Design of a serious games to improve resilience skills in youngsters},

author = {Raquel Menendez-Ferreira and Javier Torregrosa and Daniel López-Fernández and Jesus Mayor},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Entertainment Computing},

volume = {40},

pages = {100462},

publisher = {Elsevier},

keywords = {Emotional Intelligence, Extremism, Psychology, Serious Games, Videogames},

pubstate = {published},

tppubtype = {article}

}

@article{pathogens11060662,

title = {SARS-CoV-2 Mutant Spectra at Different Depth Levels Reveal an Overwhelming Abundance of Low Frequency Mutations},

author = {Brenda Martínez-González and María Eugenia Soria and Lucía Vázquez-Sirvent and Cristina Ferrer-Orta and Rebeca Lobo-Vega and Pablo Mínguez and Lorena Fuente and Carlos Llorens and Beatriz Soriano and Ricardo Ramos-Ruíz and Marta Cortón and Rosario López-Rodríguez and Carlos García-Crespo and Pilar Somovilla and Antoni Durán-Pastor and Isabel Gallego and Ana Isabel Ávila and Maria Soledad Delgado and Federico Morán and Cecilio López-Galíndez and Jordi Gómez and Luis Enjuanes and Llanos Salar-Vidal and Mario Esteban-Muñoz and Jaime Esteban and Ricardo Fernández-Roblas and Ignacio Gadea and Carmen Ayuso and Javier Ruíz-Hornillos and Nuria Verdaguer and Esteban Domingo and Celia Perales},

url = {https://www.mdpi.com/2076-0817/11/6/662},

doi = {10.3390/pathogens11060662},

issn = {2076-0817},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Pathogens},

volume = {11},

number = {6},

abstract = {Populations of RNA viruses are composed of complex and dynamic mixtures of variant genomes that are termed mutant spectra or mutant clouds. This applies also to SARS-CoV-2, and mutations that are detected at low frequency in an infected individual can be dominant (represented in the consensus sequence) in subsequent variants of interest or variants of concern. Here we briefly review the main conclusions of our work on mutant spectrum characterization of hepatitis C virus (HCV) and SARS-CoV-2 at the nucleotide and amino acid levels and address the following two new questions derived from previous results: (i) how is the SARS-CoV-2 mutant and deletion spectrum composition in diagnostic samples, when examined at progressively lower cut-off mutant frequency values in ultra-deep sequencing; (ii) how the frequency distribution of minority amino acid substitutions in SARS-CoV-2 compares with that of HCV sampled also from infected patients. The main conclusions are the following: (i) the number of different mutations found at low frequency in SARS-CoV-2 mutant spectra increases dramatically (50- to 100-fold) as the cut-off frequency for mutation detection is lowered from 0.5% to 0.1%, and (ii) that, contrary to HCV, SARS-CoV-2 mutant spectra exhibit a deficit of intermediate frequency amino acid substitutions. The possible origin and implications of mutant spectrum differences among RNA viruses are discussed.},

keywords = {Health, Self-organizing map},

pubstate = {published},

tppubtype = {article}

}

@article{villar2022virality,

title = {Virality, only the tip of the iceberg: ways of spread and interaction around COVID-19 misinformation in Twitter},

author = {Guillermo Villar-Rodríguez and Mónica Souto-Rico and Alejandro Martín},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Communication & Society},

pages = {239--256},

keywords = {Disinformation},

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 = {Deep Learning, Ensemble, Solar Radio Flux, Space, Time Series},

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 = {Dynamic Community Detection, Dynamic Problems, Evolutionary Computation, Genetic Algorithms, Social Networks Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1128/Spectrum.01459-21,

title = {A Two-Level, Intramutant Spectrum Haplotype Profile of Hepatitis C Virus Revealed by Self-Organized Maps},

author = {Maria Soledad Delgado and Celia Perales and Carlos García-Crespo and María Eugenia Soria and Isabel Gallego and Ana Isabel Ávila and Brenda Martínez-González and Lucía Vázquez-Sirvent and Cecilio López-Galíndez and Federico Morán and Esteban Domingo and Benjamin W. Neuman},

doi = {10.1128/Spectrum.01459-21  URL = https://journals.asm.org/doi/abs/10.1128/Spectrum.01459-21},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Microbiology Spectrum},

volume = {9},

number = {3},

pages = {e01459-21},

abstract = {The study provides for the first time the haplotype profile and its variation in the course of its adaptation to a cell culture environment in the absence of external selective constraints. The deep sequencing-based self-organized maps document a two-layer haplotype distribution with an ample basal platform and a lower number of protruding peaks. RNA viruses replicate as complex mutant spectra termed viral quasispecies. The frequency of each individual genome in a mutant spectrum depends on its rate of generation and its relative fitness in the replicating population ensemble. The advent of deep sequencing methodologies allows for the first-time quantification of haplotype abundances within mutant spectra. There is no information on the haplotype profile of the resident genomes and how the landscape evolves when a virus replicates in a controlled cell culture environment. Here, we report the construction of intramutant spectrum haplotype landscapes of three amplicons of the NS5A-NS5B coding region of hepatitis C virus (HCV). Two-dimensional (2D) neural networks were constructed for 44 related HCV populations derived from a common clonal ancestor that was passaged up to 210 times in human hepatoma Huh-7.5 cells in the absence of external selective pressures. The haplotype profiles consisted of an extended dense basal platform, from which a lower number of protruding higher peaks emerged. As HCV increased its adaptation to the cells, the number of haplotype peaks within each mutant spectrum expanded, and their distribution shifted in the 2D network. The results show that extensive HCV replication in a monotonous cell culture environment does not limit HCV exploration of sequence space through haplotype peak movements. The landscapes reflect dynamic variation in the intramutant spectrum haplotype profile and may serve as a reference to interpret the modifications produced by external selective pressures or to compare with the landscapes of mutant spectra in complex in vivo environments. IMPORTANCE The study provides for the first time the haplotype profile and its variation in the course of virus adaptation to a cell culture environment in the absence of external selective constraints. The deep sequencing-based self-organized maps document a two-layer haplotype distribution with an ample basal platform and a lower number of protruding peaks. The results suggest an inferred intramutant spectrum fitness landscape structure that offers potential benefits for virus resilience to mutational inputs.},

keywords = {Data Projection, Health, Self-organizing map},

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 = {Data Analysis, eLearning, Neural Networks, Self-organizing map, Unsupervised},

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 = {Clustering, Neural Networks, Self-organizing map, Unsupervised},

pubstate = {published},

tppubtype = {article}

}

@article{liz2021ensemblesb,

title = {Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis},

author = {Helena Liz López and Manuel Sánchez-Monta nés and Alfredo Tagarro and Sara Domínguez-Rodríguez and Ron Dagan and David Camacho},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Future Generation Computer Systems},

volume = {122},

pages = {220--233},

publisher = {Elsevier},

keywords = {Health},

pubstate = {published},

tppubtype = {article}

}

@article{PEREZARACIL2021100958,

title = {Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and A. M. Hernández-Díaz and E. Pereira and David Camacho and Sancho Salcedo-Sanz},

url = {https://www.sciencedirect.com/science/article/pii/S2210650221001206},

doi = {https://doi.org/10.1016/j.swevo.2021.100958},

issn = {2210-6502},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Swarm and Evolutionary Computation},

volume = {67},

pages = {100958},

abstract = {This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch’s geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.},

keywords = {CRO-SL, Evolutionary Computation, Memetic Algorithms, Nonlinear analysis, Submerged arches},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimum Shape Design of Geometrically Nonlinear Submerged Arches Using the Coral Reefs Optimization with Substrate Layers Algorithm},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and Alejandro Mateo Hernández-Díaz and Emiliano Pereira and Sancho Salcedo-Sanz},

doi = {app11135862},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Applied Sciences},

volume = {11},

number = {13},

keywords = {Coral Reefs Optimization, Ensemble},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm},

author = {Carolina Gil Marcelino and C. Camacho-Gómez and Silvia Jiménez-Fernández and Sancho Salcedo-Sanz},

doi = {en14092443},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Energies},

volume = {14},

number = {9},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimal Location and Sizing of Energy Storage Systems in DC-Electrified Railway Lines Using a Coral Reefs Optimization Algorithm with Substrate Layers},

author = {David Roch-Dupréa and C. Camacho-Gómez and Asunción P Cucala and Silvia Jiménez-Fernández and Álvaro López-López and Antonio Portilla-Figueras and Ramón R Pecharromán and Antonio Fernández-Cardador and Sancho Salcedo-Sanz},

doi = {10.3390/en14164753},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Energies},

volume = {14},

number = {16},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Eliminating Stick-Slip Vibrations in Drill-Strings with a Dual-Loop Control Strategy Optimised by the CRO-SL Algorithm},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and Emiliano Pereira and Vahid Vaziri and Sumeet S Aphale and Sancho Salcedo-Sanz},

doi = {10.3390/math9131526},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Mathematics},

volume = {8},

number = {13},

keywords = {Coral Reefs Optimization},

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 = {Decision Making, Fuzzy methods, Planning, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {article}

}

@article{menendez2020diseno,

title = {Diseno de un juego serio para la prevención de la polarización en menores},

author = {Raquel Menendez-Ferreira and Javier Torregrosa and David Camacho},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

keywords = {Emotional Intelligence, Extremism, Psychology, Serious Games, Videogames},

pubstate = {published},

tppubtype = {article}

}

@article{menendez2020improving,

title = {Improving Youngsters’ Resilience Through Video Game-Based Interventions},

author = {Raquel Menéndez-Ferreira and Javier Torregrosa and Ángel Panizo-LLedot and Antonio González-Pardo and David Camacho},

doi = {10.1142/S2196888820500153},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Vietnam Journal of Computer Science},

volume = {7},

number = {03},

pages = {263--279},

publisher = {World Scientific},

keywords = {Emotional Intelligence, Extremism, Psychology, Serious Games, Videogames},

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 = {Big Data, Data Science, Social Networks, Social Networks Analysis, Visualization methods},

pubstate = {published},

tppubtype = {article}

}

@article{torregrosa2020analyzing,

title = {Analyzing the relationship between relevance and extremist discourse in an alt-right network on Twitter},

author = {Javier Torregrosa and Ángel Panizo-Lledot and Gema Bello-Orgaz and David Camacho},

doi = { 10.1007/s13278-020-00676-1},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Social Network Analysis and Mining},

volume = {10},

number = {1},

pages = {1--17},

publisher = {Springer},

keywords = {Centrality, Extremism, Far-right, Linguistics, Psychology, Social Networks Analysis},

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 = {Dynamic Community Detection, Dynamic Problems, Genetic Algorithms, Multi-objective Optimization, Social Networks Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{9104953,

title = {Weighted ABG: A General Framework for Optimal Combination of ABG Path-Loss Propagation Models},

author = {David Casillas-Perez and C. Camacho-Gómez and Silvia Jiménez-Fernandez and Jose A. Portilla-Figueras and Sancho Salcedo-Sanz},

doi = {10.1109/ACCESS.2020.2999206},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {101758-101769},

keywords = {5G},

pubstate = {published},

tppubtype = {article}

}

@article{9270000,

title = {Submerged Arches Optimal Design With a Multi-Method Ensemble Meta-Heuristic Approach},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and Alejandro Mateo Hernández-Díaz and Emiliano Pereira and Sancho Salcedo-Sanz},

doi = {10.1109/ACCESS.2020.3040479},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {IEEE Access},

volume = {8},

pages = {215057-215072},

keywords = {Bio-inspired Metaheuristics, Ensemble},

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 = {Evolutionary Computation, Wind power},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL ensemble},

author = {C. Camacho-Gómez and Rocío Sánchez Montero and Diego Martínez Villanueva and Pablo Luís López Espí and Sancho Salcedo-Sanz},

doi = {app10031168},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {3},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A Coral Reefs Optimization algorithm with substrate layer for robust Wi-Fi channel assignment},

author = {C. Camacho-Gómez and Ivan Marsa-Maestre and Jose Manuel Gimenez-Guzman and Sancho Salcedo-Sanz},

doi = {s00500-019-03815-9},

year  = {2019},

date = {2019-12-01},

urldate = {2019-12-01},

journal = {Soft Computing},

volume = {23},

number = {23},

pages = {12621-12640},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm},

author = {C. Camacho-Gómez and Silvia Jiménez-Fernández and R Mallol-Poyato and Javier Del Ser and Sancho Salcedo-Sanz},

doi = {s00500-018-3300-0},

year  = {2019},

date = {2019-08-01},

urldate = {2019-08-01},

journal = {Soft Computing},

volume = {23},

number = {15},

pages = {6495-6510},

keywords = {Harmony Search, Renewable Energies},

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 = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

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 = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

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 = {Clustering, Growing cell structures, Health, Self-organizing map},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201915,

title = {Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods},

author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and E. Alexandre and L. Carro-Calvo and F. Jaume-Santero},

url = {https://www.sciencedirect.com/science/article/pii/S0921818118306039},

doi = {https://doi.org/10.1016/j.gloplacha.2019.04.013},

issn = {0921-8181},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Global and Planetary Change},

volume = {178},

pages = {15-34},

abstract = {In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.},

keywords = {Analogue method, Coral Reefs Optimization, Most representative points, Temperature fields reconstruction},

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 = {Coral Reefs Optimization, Multi-objective Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{ramirez2018extending,

title = {Extending QGroundControl for automated mission planning of UAVs},

author = {Cristian Ramirez-Atencia and David Camacho},

url = {https://www.mdpi.com/1424-8220/18/7/2339},

doi = {10.3390/s18072339},

issn = {1424-8220},

year  = {2018},

date = {2018-07-18},

journal = {Sensors},

volume = {18},

number = {7},

pages = {2339},

publisher = {MDPI},

abstract = {Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.},

keywords = {Computer-based Simulation, Human-Machine Interaction, Planning, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201879,

title = {An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia},

author = {Sancho Salcedo-Sanz and Ravinesh C. Deo and Laura Cornejo-Bueno and C. Camacho-Gómez and Sujan Ghimire},

url = {https://www.sciencedirect.com/science/article/pii/S0306261917314976},

doi = {https://doi.org/10.1016/j.apenergy.2017.10.076},

issn = {0306-2619},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Applied Energy},

volume = {209},

pages = {79-94},

abstract = {This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications.},

keywords = {Coral Reefs Optimization, Extreme Learning Machines, Grouping Genetic Algorithms, Hybrid modelling system, Renewable Energies, Solar radiation estimation},

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 = {Active vibration control, Bio-inspired Metaheuristics, Co-evolution, Coral Reefs Optimization, Human-induced vibrations, MIMO control, Single-objective Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ2018434,

title = {Efficient fractal-based mutation in evolutionary algorithms from iterated function systems},

author = {Sancho Salcedo-Sanz and A. Aybar-Ruíz and C. Camacho-Gómez and E. Pereira},

url = {https://www.sciencedirect.com/science/article/pii/S1007570417302915},

doi = {https://doi.org/10.1016/j.cnsns.2017.08.010},

issn = {1007-5704},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Communications in Nonlinear Science and Numerical Simulation},

volume = {56},

pages = {434-446},

abstract = {In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.},

keywords = {Evolutionary programming, Fractals, Iterated function systems, Mutation procedures design},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ20181111,

title = {Wind power field reconstruction from a reduced set of representative measuring points},

author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and A. Aybar-Ruíz and E. Alexandre},

url = {https://www.sciencedirect.com/science/article/pii/S0306261918310304},

doi = {https://doi.org/10.1016/j.apenergy.2018.07.003},

issn = {0306-2619},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Applied Energy},

volume = {228},

pages = {1111-1121},

abstract = {In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem’s objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere mid-latitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of the field), obtaining points mainly located at the North of Scandinavia (which may be associated with the circumpolar circulation), and some points in the Eastern Mediterranean, which seem to be related to the Etesian winds. Reconstructions at seasonal scales show similar results, which provides confidence on the robustness of the proposed method. The proposed methodology can be further applied to alternative energy-related problems, such as the selection of critical energy infra-structures or the selection of critical points for climate change studies, among others.},

keywords = {Analogue method, Coral Reefs Optimization, Representative points, Wind power, Wind power field reconstruction},

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 = {Constraint Satisfaction Problems, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

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 = {Constraint Satisfaction Problems, Genetic Algorithms, Multi-objective Optimization, Planning, Unmanned Aircraft Systems},

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 = {Behavioral Patterns, Clustering, Industry, Operator, Performance Metrics, Unmanned Aircraft Systems},

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 = {Clustering, Operator, Performance Metrics, Simulation-based Training, Time Series, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {article}

}

@article{Rodriguez-Fernandez2017b,

title = {Automatic Procedure Following Evaluation using Petri Net-based Workflows},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {IEEE Transactions on Industrial Informatics},

volume = {In press},

keywords = {Operator, Petri-Net, Unmanned Aircraft Systems},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez2017modellingbb,

title = {Modelling Behaviour in UAV Operations Using Higher Order Double Chain Markov Models},

author = {Víctor Rodríguez-Fernández and Antonio Gonzalez-Pardo and David Camacho},

url = {https://doi.org/10.1109/MCI.2017.2742738},

doi = {10.1109/MCI.2017.2742738},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {IEEE Computational Intelligence Magazine},

volume = {12},

number = {4},

pages = {28--37},

publisher = {IEEE},

keywords = {Behavioral Patterns, Hidden Markov Model, Operator, Unmanned Aircraft Systems},

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 = {Deep Learning, Evolutionary Computation},

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 = {Clustering, Metabolic network, Self-organizing map, Topology preserving, Unsupervised},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201762,

title = {Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm},

author = {Sancho Salcedo-Sanz and C. Camacho-Gómez and A. Magdaleno and E. Pereira and A. Lorenzana},

url = {https://www.sciencedirect.com/science/article/pii/S0022460X17300391},

doi = {https://doi.org/10.1016/j.jsv.2017.01.019},

issn = {0022-460X},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Journal of Sound and Vibration},

volume = {393},

pages = {62-75},

abstract = {In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.},

keywords = {Bio-inspired Metaheuristics, Co-evolution, Coral Reefs Optimization, Structural vibration control, Tuned Mass Dampers},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids},

author = {Sancho Salcedo-Sanz and C. Camacho-Gómez and R Mallol-Poyato and Silvia Jiménez-Fernández and Javier Del Ser},

doi = {s00500-016-2295-7},

year  = {2016},

date = {2016-11-01},

urldate = {2016-11-01},

journal = {Soft Computing},

volume = {20},

number = {11},

pages = {4287-4300},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{Rodriguez-Fernandez2016,

title = {Automatic profile generation for UAV operators using a simulation-based training environment},

author = {Víctor Rodríguez-Fernández and Héctor D Menéndez and David Camacho},

url = {http://link.springer.com/10.1007/s13748-015-0072-y},

doi = {10.1007/s13748-015-0072-y},

issn = {2192-6352},

year  = {2016},

date = {2016-02-01},

urldate = {2016-02-01},

journal = {Progress in Artificial Intelligence},

volume = {5},

number = {1},

pages = {37--46},

keywords = {Operator, Simulation-based Training, Unmanned Aircraft Systems},

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 = {Malware Detection, Multi-objective Optimization},

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 = {Clustering, Distance Measures, Growing cell structures, Self-organizing map},

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 = {Ant Colony Optimization, Constraint Satisfaction Problems, Graph Theory, Swarm Intelligence, Videogames},

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 = {Clustering, Genetic Algorithms, Graph Theory},

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 = {https://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 = {Computer-based Education, Data Mining, Normalized Compression Distance, Virtual World},

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 = {https://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 = {Classification, Clustering, Collective Trends, Social Networks, Twitter},

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 = {Data Mining, Social Networks, Twitter},

pubstate = {published},

tppubtype = {article}

}

@article{okazaki2014mine,

title = {How to mine brand Tweets: Procedural guidelines and pretest},

author = {Shintaro Okazaki and Ana M Díaz-Martín and Mercedes Rozano and Héctor D Menéndez},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {International Journal of Market Research},

volume = {56},

number = {4},

pages = {467--488},

publisher = {The Market Research Society},

keywords = {Classification, Sentimental Analysis, Twitter},

pubstate = {published},

tppubtype = {article}

}

@article{Lorenzo-Redondo2014,

title = {Realistic Three Dimensional Fitness Landscapes Generated by Self Organizing Maps for the Analysis of Experimental HIV-1 Evolution},

author = {Ramón Lorenzo-Redondo and Maria Soledad Delgado and Federico Morán and Cecilio Lopez-Galindez},

url = {https://plos.figshare.com/articles/dataset/_Realistic_Three_Dimensional_Fitness_Landscapes_Generated_by_Self_Organizing_Maps_for_the_Analysis_of_Experimental_HIV_1_Evolution_/948357},

doi = {10.1371/journal.pone.0088579},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

keywords = {Data Projection, Health, Self-organizing map},

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 = {https://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 = {Business Intelligence, Data Mining, Genetic Algorithms, Graph Theory},

pubstate = {published},

tppubtype = {article}

}

@article{granados13,

title = {Improving NCD accuracy by combining document segmentation and document distortion},

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

url = {http://dx.doi.org/10.1007/s10115-013-0664-4},

issn = {0219-1377},

year  = {2013},

date = {2013-06-06},

urldate = {2013-06-06},

journal = {Knowledge and Information Systems},

pages = {1-23},

publisher = {Springer-Verlag},

keywords = {Data Compression, Document representation, Information Filtering, Information Theory, Word Removal},

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 = {Clustering, Information Extraction, Knowledge-based Systems},

pubstate = {published},

tppubtype = {article}

}