Next Article in Journal
Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
Next Article in Special Issue
2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization
Previous Article in Journal
Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems
Previous Article in Special Issue
An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
Open AccessArticle

A Selection Process for Genetic Algorithm Using Clustering Analysis

Université du Québec à Chicoutimi, 555 boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut, Lebanon
LISV Laboratory, University of Versailles Saint-Quentin en-Yvelines, 10-12 Avenue de l’Europe, 78140 Vélizy, France
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.
Algorithms 2017, 10(4), 123;
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)
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
Show Figures

Figure 1

MDPI and ACS Style

Chehouri, A.; Younes, R.; Khoder, J.; Perron, J.; Ilinca, A. A Selection Process for Genetic Algorithm Using Clustering Analysis. Algorithms 2017, 10, 123.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop