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

D. Molina, J. Poyatos, J. Ser, S. García, A. Hussain, and F. Herrera. CoRR, (2020)

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Javier Manero

Javier Jáurequi

Javier Dreyer

Javier Papa

Javier Arpón

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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations.D. Molina, J. Poyatos, J. Ser, S. García, A. Hussain, and F. Herrera. Cogn. Comput., 12 (5): 897-939 (2020)Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations.D. Molina, J. Poyatos, J. Ser, S. García, A. Hussain, and F. Herrera. CoRR, (2020)A prescription of methodological guidelines for comparing bio-inspired optimization algorithms.A. LaTorre, D. Molina, E. Osaba, J. Poyatos, J. Ser, and F. Herrera. Swarm Evol. Comput., (2021)General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.I. Triguero, D. Molina, J. Poyatos, J. Ser, and F. Herrera. Inf. Fusion, (March 2024)Multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness.J. Poyatos, D. Molina, A. Martínez-Seras, J. Ser, and F. Herrera. Appl. Soft Comput., (November 2023)Lights and shadows in Evolutionary Deep Learning: Taxonomy, critical methodological analysis, cases of study, learned lessons, recommendations and challenges.A. Martinez, J. Ser, E. Villar-Rodriguez, E. Osaba, J. Poyatos, S. Tabik, D. Molina, and F. Herrera. Inf. Fusion, (2021)Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges.A. Martinez, J. Ser, E. Villar-Rodriguez, E. Osaba, J. Poyatos, S. Tabik, D. Molina, and F. Herrera. CoRR, (2020)EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks.J. Poyatos, D. Molina, A. Martinez, J. Ser, and F. Herrera. CoRR, (2022)EvoPruneDeepTL: An evolutionary pruning model for transfer learning based deep neural networks.J. Poyatos, D. Molina, A. Martinez, J. Ser, and F. Herrera. Neural Networks, (January 2023)More is not Always Better: Insights from a Massive Comparison of Meta-heuristic Algorithms over Real-Parameter Optimization Problems.J. Ser, E. Osaba, A. Martinez, M. Bilbao, J. Poyatos, D. Molina, and F. Herrera. SSCI, page 1-7. IEEE, (2021)

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Disambiguation of "Poyatos, Javier"

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

Please choose a person to relate this publication to

Javier Manero

Javier Jáurequi

Javier Dreyer

Javier Papa

Javier Arpón

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Poyatos, Javier"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations.

Please choose a person to relate this publication to

Javier Manero

Javier Jáurequi

Javier Dreyer

Javier Papa

Javier Arpón

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations.