Javier Poyatos
Orcid: 0000-0001-7957-0644
According to our database1,
Javier Poyatos
authored at least 10 papers
between 2020 and 2024.
Collaborative distances:
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Bibliography
2024
General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.
Inf. Fusion, March, 2024
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects.
CoRR, 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