Javier Poyatos
According to our database1,
Javier Poyatos
authored at least 9 papers
between 2020 and 2024.
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Bibliography
2024
General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.
Inf. Fusion, March, 2024
2023
Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness.
Appl. Soft Comput., November, 2023
EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks.
Neural Networks, January, 2023
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications.
CoRR, 2023
2021
A prescription of methodological guidelines for comparing bio-inspired optimization algorithms.
Swarm Evol. Comput., 2021
Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges.
Inf. Fusion, 2021
More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021
2020
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations.
CoRR, 2020
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations.
Cogn. Comput., 2020