407 citations · 1k across the 17 of their papers we have counts for
8 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…
CURIE: A Cellular Automaton for Concept Drift Detection
Jesus L. Lobo, Javier Del Ser, Eneko Osaba +2
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, g…
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…
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…
LUNAR: Cellular Automata for Drifting Data Streams
Jesus L. Lobo, Javier Del Ser, Francisco Herrera
With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world a…
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations (from 2020 to 2024)
Daniel Molina, Javier Poyatos, Javier Del Ser +3
In recent years, bio-inspired optimization methods, which mimic biological processes to solve complex problems, have gained popularity in recent literature. The proliferation of pr…