International Journal of Computational Intelligence Systems

Volume 10, Issue 1, 2017, Pages 1186 - 1197

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Authors
Javier Arellano-Verdejo1, *, javier_arellano_verdejo@hotmail.com, Salvador Godoy-Calderon1, sgodoyc@cic.ipn.mx, Federico Alonso-Pecina2, federico.alonso@uaem.mx, Adolfo Guzmán Arenas1, a.guzman@acm.org, Marco Antonio Cruz-Chavez2, mcruz@uaem.mx
*Departament of Computer Sciences and Languages, Universidad de Málaga, Málaga Spain
Corresponding Author
Received 19 October 2016, Accepted 15 August 2017, Available Online 30 August 2017.
DOI
10.2991/ijcis.10.1.78How to use a DOI?
Keywords
Evolutionary Algorithms; Parallel Heuristics; Global Optimization; Parallel Genetic Algorithm; Heuristic Spatially Structured; Island Genetic Algorithm
Abstract

In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
1186 - 1197
Publication Date
2017/08/30
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.10.1.78How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Javier Arellano-Verdejo
AU  - Salvador Godoy-Calderon
AU  - Federico Alonso-Pecina
AU  - Adolfo Guzmán Arenas
AU  - Marco Antonio Cruz-Chavez
PY  - 2017
DA  - 2017/08/30
TI  - A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms
JO  - International Journal of Computational Intelligence Systems
SP  - 1186
EP  - 1197
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.10.1.78
DO  - 10.2991/ijcis.10.1.78
ID  - Arellano-Verdejo2017
ER  -