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.
Highlights
The fields of computational intelligence and optimization algorithms have grown rapidly in the past few decades
We propose a genetic algorithm (GA)-based algorithm that uses clustering analysis to organize the population and select the parents for recombination
A system with an Intel core i7 2.9 GHz processor and 4.096 GB RAM is used for implementing the MATLAB code for the proposed k-means genetic algorithm selection process (KGA) techniques
Summary
The fields of computational intelligence and optimization algorithms have grown rapidly in the past few decades. Classical methods are not efficient in solving current problems in engineering such as energy, transportation and management [1]. The development of these optimization algorithms can be mainly divided into deterministic and stochastic approaches [2]. Most conventional algorithms are deterministic, such as gradient-based algorithms that use the function values and their derivatives. We propose a genetic algorithm (GA)-based algorithm that uses clustering analysis to organize the population and select the parents for recombination. The remainder of the paper is organized as follows: The section summarizes some relevant studies that have explored clustering analysis in optimization algorithms.
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