A Novel Methodology for Genetic Algorithms in Crossover Operation: Segment Replacement Opeartor

Genetic Algorithms (GA) are robust and efficient search and optimization techniques inspired by Darwin's theory of natural evolution. GA is composed of genetic operators and genetic parameters. Most of the approaches are random in nature which reduces the performance of the simple genetic algorithm. In this paper, a novel approach for crossover operator called segment replacement Operator (SRO) is attempted. In order to evaluate the efficiency and feasibility of the proposed technique, TSP problem is chosen and a comparison between the results of this study used in GAs is made through a number of test experiments with various parameter settings. Results of this study clearly show the significant differences between the proposed operator and the other existing operator techniques