JULIO
ORTEGA LOPERA
Investigador en el periodo 2006-2021
MIGUEL
DAMAS HERMOSO
CATEDRÁTICO DE UNIVERSIDAD
Publicaciones en las que colabora con MIGUEL DAMAS HERMOSO (35)
2021
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A lexicographic cooperative co-evolutionary approach for feature selection
Neurocomputing, Vol. 463, pp. 59-76
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Energy-Time Profiling for Machine Learning Methods to EEG Classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Recurrent Neural Networks and Efficiency in High-Dimensional EEG Classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
2020
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A parallel and distributed multi-population GA with asynchronous migrations: Energy-time analysis for heterogeneous systems
GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
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Cost-Efficiency of Convolutional Neural Networks for High-Dimensional EEG Classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Deep learning for EEG-based motor imagery classification: Accuracy-cost trade-off
PLoS ONE, Vol. 15, Núm. 6
2019
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A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI
Neurocomputing, Vol. 333, pp. 407-418
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Annálisis Energía-Tiempo de Redes Neuronales Convolucionales Distribuidas en Clusters Heterogéneos para clasificación de EEGs
Avances en Arquitectura y Tecnología de Computadores: Actas de Jornadas SARTECO, Cáceres, 18 a 20 de septiembre de 2019| (Servicio de Publicaciones), pp. 109-115
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Energy-Time Analysis of Convolutional Neural Networks Distributed on Heterogeneous Clusters for EEG Classification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures
Concurrency and Computation: Practice and Experience
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Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Time-energy analysis of multilevel parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks
Journal of Supercomputing, Vol. 75, Núm. 7, pp. 3397-3425
2018
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A power-performance perspective to multiobjective electroencephalogram feature selection on heterogeneous parallel platforms
Journal of Computational Biology
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Multi-objective feature selection for EEG classification with multi-level parallelism on heterogeneous CPU-GPU clusters
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
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Prediction of energy consumption in a NSGA-II-based evolutionary algorithm
GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
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Speedup and energy analysis of EEG classification for BCI tasks on CPU-GPU clusters
ACM International Conference Proceeding Series
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Uso y difusión de la plataforma educativa SWAD / OpenSWAD en la Universidad de Granada y en el mundo
Enseñanza y aprendizaje de ingeniería de computadores: Revista de Experiencias Docentes en Ingeniería de Computadores, Núm. 8, pp. 117-144
2017
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A parallel island approach to multiobjective feature selection for brain-computer interfaces
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10305 LNCS, pp. 16-27
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Assessing Energy Consumption and Runtime Efficiency of Master- Worker Parallel Evolutionary Algorithms in CPU-GPU Systems
Annals of Multicore and GPU Programming: AMGP, Vol. 4, Núm. 1, pp. 23-36