Implementation of generative crossover operator in genetic algorithm to solve traveling salesman problem

Abstract

The research work aims to solve symmetric traveling salesman problem more efficiently. In this research paper, a different crossover operator is proposed, which produces 18 valid offsprings from two parents. The performance of proposed crossover operator is compared with three other existing crossover operators by maintaining the selection technique, mutation technique, and fitness function identical. This crossover operator is tested with data from TSP dataset. The intercity distance table of cities in which distance is measured with L1 norm formed the input to the coded C program that implemented the proposed crossover operator. The same dataset was used to compare the performance of this crossover operator with other three crossover operators. The comparative study indicates that proposed crossover operator performs well compared to other crossover operators in solving traveling salesman problem.