Hybrid Genetic Algorithm and Mixed Crossover Operator for Optimizing TSP

Genetic Algorithms (GAs) are the search algorithms and optimization techniques based on the mechanics of natural selection and natural genetics. They sort out interesting areas of a space quickly but without guaranteeing more convergence. So GA may be mixed with various local problem-specific search techniques to form a hybrid that will combine the globality and parallelism of GA with more convergence behavior of local search technique. In this paper, a simple genetic algorithm and a genetic algorithm with changing crossover is used. This paper proposes hybrid algorithms in which hill climbing is applied on each individual selected by selection operator for reproduction. The experiments have been conducted using three different inputs from TSPLIB provided by Heidelberg University and implementation is carried out using MATLAB. The result shows that the proposed algorithms perform better than the simple genetic algorithm in terms of producing more optimal results.