论文信息 - Hybrid Genetic Algorithm: Traveling Salesman Problem

Hybrid Genetic Algorithm: Traveling Salesman Problem

A genetic algorithm has three main operators namely selection, crossover and mutation. Each operator has various sub operators. Selection of sub operator that can be applied on particular problem is difficult task. Thus this paper proposes a hybrid genetic algorithm (HGA). HGA algorithm finds the sub operators that can be applied on traveling salesman problem. After that it finds the threshold value. Based on threshold value it switches from one sub operator to other sub operator. The HGA algorithm score over existing genetic algorithm on traveling salesman problem on large number of cities.

[1] Lance D. Chambers,et al. Practical Handbook of Genetic Algorithms , 1995 .

[2] S. N. Sivanandam,et al. Introduction to genetic algorithms , 2007 .

[3] Padmavathi Kora,et al. Crossover Operators in Genetic Algorithms: A Review , 2017 .

[4] K. Katayama,et al. The efficiency of hybrid mutation genetic algorithm for the travelling salesman problem , 2000 .

[5] P Willett,et al. Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[6] Heinz Mühlenbein,et al. Evolution algorithms in combinatorial optimization , 1988, Parallel Comput..

[7] Goldberg,et al. Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[8] David E. Goldberg,et al. Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[9] Zbigniew Michalewicz,et al. Handbook of Evolutionary Computation , 1997 .

[10] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[11] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12] Gerhard Reinelt,et al. TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..