https://hdl.handle.net/10481/45178 2026-04-12T00:40:31Z https://hdl.handle.net/10481/92001 Energy-aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms Escobar Pérez, Juan José; Rodríguez, Francisco; Savran Kiziltepe, Rukiye; Prieto Campos, Beatriz; Kimovski, Dragi; Ortiz, Andrés; Damas Hermoso, Miguel The growing energy consumption caused by IT is forcing application developers to consider energy efficiency as one of the fundamental design parameters. This parameter acquires great relevance in HPC systems when running artificial neural networks and Machine Learning applications. Thus, this article shows an example of how to estimate and consider energy consumption in a real case of EEG classification. An efficient and distributed implementation of the KNN algorithm that uses mRMR as a feature selection technique to reduce the dimensionality of the dataset is proposed. The performance of three different workload distributions is analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works. It achieves an accuracy rate of 88.8% and a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy of the sequential version. https://hdl.handle.net/10481/57947 Many-Objective Cooperative Co-evolutionary Feature Selection: A Lexicographic Approach González, Jesus; Ortega Lopera, Julio; Damas Hermoso, Miguel; Martín Smith, Pedro Jesús This paper presents a new wrapper method able to optimize simultaneously the parameters of the classifier while the size of the subset of features that better describe the input dataset is also being minimized. The search algorithm used for this purpose is based on a co-evolutionary algorithm optimizing several objectives related with different desirable properties for the final solutions, such as its accuracy, its final number of features, and the generalization ability of the classifier. Since these objectives can be sorted according to their priorities, a lexicographic approach has been applied to handle this many-objective problem, which allows the use of a simple evolutionary algorithm to evolve each one of the different sub-populations.