Modified genetic algorithm with novel crossover and mutation operator for travelling salesman problem

Abstract

In this study, Genetic Algorithm (GA), a sort of randomized direct, iterative search methodology built around natural selection, is employ in computers to discover approximations of solutions to optimisation and search issues. GA employs operators including selection, crossover, and mutation to tackle. In case of NP-hard issues, particularly for travelling salesman problem (TSP), the GAs is beneficial. To reduce the overall distance, we propose a novel crossover operator with its python code for the TSP. Along with the Python pseudo coding, we additionally introduced a mutation operator to enhance the consummation of GA in determining the shortest distance in the TSP. To emphasize the proposed crossover and mutation operator, we also illustrate different steps using examples. We integrated path representation with our developed crossover and mutation operator as it is apparent method to represent a tour.