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.
KeywordsHybrid genetic algorithms Traveling salesman problem Genetic algorithms Combinational optimization
The author gratefully thankful to Rishab Rakshit student of SMIT who did simulation in summer project’16 at MUJ.
- 1.Goldberg DE (2006) Genetic algorithms. Pearson Education, IndiaGoogle Scholar
- 10.Holland JH (1975) Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. University of Michigan Press, USAGoogle Scholar
- 11.Padmavathi K, Yadlapalli P (2017) Crossover operators in genetic algorithms: a review. Int J Comput Appl 162(10):34–36Google Scholar