| Comprehensive taxonomies of nature-and bio-inspired optimization: Inspiration versus algorithmic behavior, critical analysis recommendations D Molina, J Poyatos, JD Ser, S García, A Hussain, F Herrera Cognitive Computation 12, 897-939, 2020 | 225 | 2020 |
| A prescription of methodological guidelines for comparing bio-inspired optimization algorithms A LaTorre, D Molina, E Osaba, J Poyatos, J Del Ser, F Herrera Swarm and Evolutionary Computation 67, 100973, 2021 | 115 | 2021 |
| Lights and shadows in evolutionary deep learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges AD Martinez, J Del Ser, E Villar-Rodriguez, E Osaba, J Poyatos, S Tabik, ... Information Fusion 67, 161-194, 2021 | 48 | 2021 |
| EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks J Poyatos, D Molina, AD Martinez, J Del Ser, F Herrera Neural Networks 158, 59-82, 2023 | 34 | 2023 |
| General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance I Triguero, D Molina, J Poyatos, J Del Ser, F Herrera Information Fusion 103, 102135, 2024 | 32 | 2024 |
| More is not always better: Insights from a massive comparison of meta-heuristic algorithms over real-parameter optimization problems J Del Ser, E Osaba, AD Martinez, MN Bilbao, J Poyatos, D Molina, ... 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2021 | 12 | 2021 |
| Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness J Poyatos, D Molina, A Martínez-Seras, J Del Ser, F Herrera Applied Soft Computing 147, 110757, 2023 | 10 | 2023 |
| General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Open Challenges and Implications I Triguero, D Molina, J Poyatos, J Del Ser, F Herrera arXiv preprint arXiv:2307.14283, 2023 | 5 | 2023 |
| A prescription of methodological guidelines for comparing bio-inspired optimization algorithms, swarm and evolutionary computation A LaTorre, D Molina, E Osaba, J Poyatos, J Del Ser, F Herrera | 5 | 2021 |
| Nature-and bio-inspired optimization: The good, the bad, the ugly and the hopeful D Molina Cabrera, J POYATOS AMADOR, E OSABA ICEDO, ... DYNA Ingeniería e Industria, 2022 | 3 | 2022 |
| Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects J Poyatos, J Del Ser, S Garcia, H Ishibuchi, D Molina, I Triguero, B Xue, ... arXiv preprint arXiv:2407.08745, 2024 | | 2024 |
| Metaheuristics for the Design of Deep Learning Models J Poyatos Amador Universidad de Granada, 2024 | | 2024 |
| EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks J Poyatos Amador, D Molina Cabrera, AD Martínez, J Del Ser, ... | | 2023 |
| Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness J Poyatos Amador, D Molina Cabrera, A Martínez-Seras, J Del Ser, ... | | 2023 |
| Optimización inspirada en la naturaleza y en la biología: lo bueno, lo malo, lo feo y lo esperanzador D Molina, J Poyatos, E Osaba, J Del Ser, F Herrera DYNA 97 (2), 114-117, 2022 | | 2022 |
| Comprehensive Taxonomies of Nature-and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations D Molina Cabrera, J Poyatos Amador, J Del Ser, S García López, ... | | 2020 |
Javier (Javi) Del SerTECNALIA, Universidad del País Vasco (UPV/EHU)Bestätigte E-Mail-Adresse bei tecnalia.com
