A Selection Process for Genetic Algorithm Using Clustering Analysis

Adam Chehouri (1, 2) , Rafic Younes (2) , Jihan Khoder (3) , Jean Perron (1) , Adrian Ilinca (4)

1 UQAC - Université du Québec à Chicoutimi
2 Université Libanaise - Faculté de Génie
3 LISV - Laboratoire d'Ingénierie des Systèmes de Versailles
4 UQAR - Université du Québec à Rimouski

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 (KGA f) and via an optimal partitioning K opt (KGA o) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.

Mots clés: selection process; genetic algorithm; clustering; k-means; optimization algorithm

Domaines: Algorithme et structure de données [cs.DS]; Langage de programmation [cs.PL]; Ingénierie, finance et science [cs.CE]

Adam Chehouri, Rafic Younes, Jihan Khoder, Jean Perron, Adrian Ilinca. A Selection Process for Genetic Algorithm Using Clustering Analysis. Algorithms, 2017, 10 (4), ⟨10.3390/a10040123⟩. ⟨hal-01654909⟩