Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol 3, No.1, 2012 10 Analyzing the Impact of Genetic Parameters on Gene Grouping Genetic Algorithm and Clustering Genetic Algorithm R.Sivaraj (Corresponding author) Research Scholar and Assistant Professor (Senior Grade) Department Of Computer Science and Engineering Velalar College of Engineering and Technology Erode, Tamil Nadu, India E-mail: rsivarajcse@gmail.com Dr.T.Ravichandran Principal and Research Supervisor Hindusthan Institute of Technology Coimbatore-641032, Tamil Nadu, India E-mail: dr.t.ravichandran@gmail.com 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. 1. Introduction Evolution is the process which enables individuals or species in one generation to modify or improve in the next generation. The nature helps individuals to adapt to the changing environment through the process of evolution. As species evolve over time, they become more complex and hold better characteristics. This process helps more fit individuals to retain in the environment and those which cannot retain the environment die and run out of species. Genetic Algorithm is of such kind which follows the “Principle of Natural Evolution and Genetics”. The basic principles of Genetic Algorithms (GA) were first laid down rigorously by Holland [1975]. They simulate those processes in natural populations which are essential to evolution. The general outline of the Standard Genetic Algorithm (Goldberg [1989]) process is given below in figure 1.Exactly which processes are essential for evolution and which processes have little or no role to play is still a matter of research, but the foundations are clear.