Finding the optimal gene order for genetic algorithm

This paper presents a hybrid algorithm to improve the efficiency of canonical genetic algorithm. It starts by introducing rationale and techniques of genetic algorithm and its drawback. An optimal gene order finding algorithm is then presented with its application to iterations, as well as the relative genetic operators. Finally, the algorithm is applied to the traveling salesman problem (TSP). After each iterating, overlap vectors from best individuals are selected as the optimal gene order and used to mark some individuals for the next iteration with carefully prepared parameters. Some data sets are chosen to investigate the performance of the hybrid algorithm and the experimental results show that it performs better than canonical genetic algorithm in some instances.