Javier Poyatos

According to our database, Javier Poyatos authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects.

Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Hisao Ishibuchi, Isaac Triguero, Bing Xue, Xin Yao, Francisco Herrera

IEEE Trans. Evol. Comput., June, 2026

2025

The paradox of success in evolutionary and bioinspired optimization: Revisiting critical issues, key studies, and methodological pathways.

Daniel Molina, Javier Del Ser, Javier Poyatos, Francisco Herrera

Swarm Evol. Comput., 2025

2024

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.

Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera

Inf. Fusion, March, 2024

2023

Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness.

Javier Poyatos, Daniel Molina, Aitor Martínez-Seras, Javier Del Ser, Francisco Herrera

Appl. Soft Comput., November, 2023

EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks.

Javier Poyatos, Daniel Molina, Aritz D. Martinez, Javier Del Ser, Francisco Herrera

Neural Networks, January, 2023

General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications.

Isaac Triguero, Daniel Molina, Javier Poyatos, Javier Del Ser, Francisco Herrera

CoRR, 2023

2021

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms.

Antonio LaTorre, Daniel Molina, Eneko Osaba, Javier Poyatos, Javier Del Ser, Francisco Herrera

Swarm Evol. Comput., 2021

Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges.

Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera

Inf. Fusion, 2021

More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems.

Javier Del Ser, Eneko Osaba, Aritz D. Martinez, Miren Nekane Bilbao, Javier Poyatos, Daniel Molina, Francisco Herrera

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.

Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera

CoRR, 2020

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations.

Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera

Cogn. Comput., 2020