A Selection Process for Genetic Algorithm Using Clustering Analysis
AbstractThis 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
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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.
Chehouri A, Younes R, Khoder J, Perron J, Ilinca A. A Selection Process for Genetic Algorithm Using Clustering Analysis. Algorithms. 2017; 10(4):123.Chicago/Turabian Style
Chehouri, Adam; Younes, Rafic; Khoder, Jihan; Perron, Jean; Ilinca, Adrian. 2017. "A Selection Process for Genetic Algorithm Using Clustering Analysis." Algorithms 10, no. 4: 123.
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