A Comparative Study of Adaptive Crossover Operators for Genetic Algorithms to Resolve the Traveling Salesman Problem

TL;DR

This paper compares various crossover operators in genetic algorithms for solving the Traveling Salesman Problem, highlighting that the OX operator yields the best solutions among those tested.

Contribution

It provides an experimental comparison of over six crossover operators specifically for the TSP, identifying the OX operator as the most effective.

Findings

OX operator outperforms other crossover methods in TSP solutions

Experimental results demonstrate the effectiveness of specific crossover operators

The study offers insights into parameter tuning for GAs in TSP contexts

Abstract

Genetic algorithm includes some parameters that should be adjusting so that the algorithm can provide positive results. Crossover operators play very important role by constructing competitive Genetic Algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the Traveling Salesman Problem (TSP) are discussed. The results of experimental comparison of more than six different crossover operators for the TSP are presented. The experiment results show that OX operator enables to achieve a better solutions than other operators tested.