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Algorithms 2017, 10(4), 123; https://doi.org/10.3390/a10040123

A Selection Process for Genetic Algorithm Using Clustering Analysis

1
Université du Québec à Chicoutimi, 555 boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
2
Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut, Lebanon
3
LISV Laboratory, University of Versailles Saint-Quentin en-Yvelines, 10-12 Avenue de l’Europe, 78140 Vélizy, France
4
Wind Energy Research Laboratory (WERL), Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, QC G5L 3A1, Canada
*
Author to whom correspondence should be addressed.
Received: 16 August 2017 / Revised: 30 October 2017 / Accepted: 31 October 2017 / Published: 2 November 2017
(This article belongs to the Special Issue Evolutionary Computation for Multiobjective Optimization)
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Abstract

This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf) and via an optimal partitioning Kopt (KGAo) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems. View Full-Text
Keywords: genetic algorithm; selection process; clustering; k-means; optimization algorithm genetic algorithm; selection process; clustering; k-means; optimization algorithm
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Chehouri, A.; Younes, R.; Khoder, J.; Perron, J.; Ilinca, A. A Selection Process for Genetic Algorithm Using Clustering Analysis. Algorithms 2017, 10, 123.

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