Analyzing the Impact of Genetic Parameters on Gene Grouping Genetic Algorithm and Clustering Genetic Algorithm

Abstract

Genetic Algorithms are stochastic randomized procedures used to solve search and optimization problems. Many multi-population and multi-objective Genetic Algorithms are introduced by researchers to achieve improved performance. Gene Grouping Genetic Algorithm (GGGA) and Clustering Genetic Algorithm (CGA) are of such kinds which are proved to perform better than Standard Genetic Algorithm (SGA). This paper compares the performance of both these algorithms by varying the genetic parameters. The results show that GGGA provides good solutions, even to large-sized problems in reasonable computation time compared to CGA and SGA.

Keywords: Stochastic, randomized, multi-population, Gene Grouping Genetic Algorithm, Clustering Genetic Algorithm.