Genetic Algorithm with Updated Multipoint Crossover Technique and its Application to TSP

Genetic Algorithm (GA) is a promising method for optimizing the NP-hard problem especially the Travelling Salesman Problem (TSP). The reason of its popularity is for the ability to gain an ideal approximation in time. GA is usually based on the three artisans namely selection, reproduction and metamorphosis. The principal target of using GA is to determine the lowest total cost to travel all the nodes optimally. Consequently, this study introduces a novel crossover operator which optimizes the solution to the TSP. The suggested method started with two randomly selected parents and new offsprings have been generated by comparing cost. The overall methods, as well as the experimental outcomes, have also depicted here. The paper concludes that the newly introduced crossover operator outperforms various cross-over operators. It produced better result while experimenting on a set of instances from TSPLIB dataset.