PERFORMANCE ANALYSIS OF GENETIC OPERATORS ON TEST FUNCTIONS OF SINGLE OBJECTIVE OPTIMIZATION PROBLEMS.

Abstract

Multi Clustered Parallel Genetic Algorithm is a type of multi population based genetic algorithm which gives equal importance to low fit individuals. It has been applied to 0/1 knapsack problem and found to perform well compared to the Standard Genetic Algorithm. This paper explores the working principle of multi clustered parallel genetic algorithm for the standard test functions for the single objective optimization problems and compared with the standard genetic algorithm. The performance is compared with the standard genetic algorithm, the standard test functions of single objective optimization problems are used and the result shows that proposed method performs better with convergence velocity