Impact of crossover probability on symmetric travel salesman problem efficiency

Abstract

Genetic algorithm (GA) is a powerful evolutionary searching technique that is used successfully to solve and optimize problems in different research areas. Genetic Algorithm (GA) considered as one of optimization methods used to solve Travel salesman Problem (TSP). The feasibility of GA in finding TSP solution is dependent on GA operators; encoding method, population size, number of generations in general. In specific, crossover and its probability play a significant role in finding possible solution for Symmetric TSP (STSP). In addition, crossover should be determined and enhanced in term reaching optimal or at least near optimal. In This paper, we spot the light on using modified crossover method called Modified sequential constructive crossover and its impact on reaching optimal solution. To justify the relevance of parameters value in solving TSP, a set comparative analysis conducted on different crossover methods values.