A selection process for genetic algorithm using clustering analysis

被引：25
作者：
Chehouri A. [1 ,2 ]
Younes R. [2 ]
Khoder J. [3 ]
Perron J. [1 ]
Ilinca A. [4 ]
机构：
[1] Université du Québec à Chicoutimi, 555 boulevard de l'Université, Chicoutimi, G7H 2B1, QC
[2] Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut
[3] LISV Laboratory, University of Versailles Saint-Quentin en-Yvelines, 10-12 Avenue de l'Europe, Vélizy
[4] Wind Energy Research Laboratory (WERL), Université du Québec à Rimouski, 300 allée des Ursulines, Rimouski, G5L 3A1, QC
来源：
Algorithms | 2017年 / 10卷 / 04期
关键词：
Clustering; Genetic algorithm; K-means; Optimization algorithm; Selection process;
D O I：
10.3390/a10040123
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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. © 2017 by the authors.
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