201 citations · 919 across the 35 of their papers we have counts for
7 papers · 1 filter
Emerging NeoHebbian Dynamics in Forward-Forward Learning: Implications for Neuromorphic Computing
Erik B. Terres-Escudero, Javier Del Ser, Pablo García-Bringas
Advances in neural computation have predominantly relied on the gradient backpropagation algorithm (BP). However, the recent shift towards non-stationary data modeling has highligh…
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…
Evolutionary Multitask Optimization: Fundamental Research Questions, Practices, and Directions for the Future
Eneko Osaba, Javier Del Ser, Ponnuthurai N. Suganthan
Transfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Tra…
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…
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…