53 citations · 76 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…