407 citations · 1k across the 17 of their papers we have counts for
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The Paradox of Success in Evolutionary and Bioinspired Optimization: Revisiting Critical Issues, Key Studies, and Methodological Pathways
Daniel Molina, Javier Del Ser, Javier Poyatos +1
Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in…
A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems
Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser +6
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research st…
Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects
Javier Poyatos, Javier Del Ser, Salvador Garcia +6
In Artificial Intelligence, there is an increasing demand for adaptive models capable of dealing with a diverse spectrum of learning tasks, surpassing the limitations of systems de…
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness
Javier Poyatos, Daniel Molina, Aitor Martínez +2
Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known…
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
Javier Poyatos, Daniel Molina, Aritz. D. Martinez +2
In recent years, Deep Learning models have shown a great performance in complex optimization problems. They generally require large training datasets, which is a limitation in most…
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Eneko Osaba, Javier Del Ser, Aritz D. Martinez +2
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem ef…
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 +5
Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyper-parame…
A Prescription of Methodological Guidelines for Comparing Bio-inspired Optimization Algorithms
Antonio LaTorre, Daniel Molina, Eneko Osaba +2
Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every…
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo +2
Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a sing…