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

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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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  -