Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem

TL;DR

This paper evaluates various mutation operators within genetic algorithms to determine their effectiveness in solving the Traveling Salesman Problem, providing insights into optimal parameter selection.

Contribution

It offers a comparative analysis of mutation operators for genetic algorithms applied to TSP, highlighting the impact of different operators on solution quality.

Findings

Certain mutation operators outperform others in TSP solutions

Optimal mutation parameters improve genetic algorithm efficiency

Discussion supports tailored operator selection for TSP

Abstract

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Travelling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.