23 citations · 23 across the 3 of their papers we have counts for
5 papers
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…
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo +2
Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking enviro…
dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems
Eneko Osaba, Aritz D. Martinez, Akemi Galvez +2
The emerging research paradigm coined as multitasking optimization aims to solve multiple optimization tasks concurrently by means of a single search process. For this purpose, the…
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…
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
Aritz D. Martinez, Eneko Osaba, Javier Del Ser +1
In recent years, Multifactorial Optimization (MFO) has gained a notable momentum in the research community. MFO is known for its inherent capability to efficiently address multiple…