On Enhancing Genetic Algorithms Using New Crossovers

TL;DR

This paper introduces new crossover operators, including a physics-inspired collision crossover, and demonstrates their effectiveness in improving genetic algorithm performance on TSP problems.

Contribution

It proposes novel crossover operators and selection strategies, significantly enhancing genetic algorithms beyond traditional methods.

Findings

Collision crossover improves GA performance

Using multiple crossover operators yields better results

Proposed methods outperform standard crossovers like PMX

Abstract

This paper investigates the use of more than one crossover operator to enhance the performance of genetic algorithms. Novel crossover operators are proposed such as the Collision crossover, which is based on the physical rules of elastic collision, in addition to proposing two selection strategies for the crossover operators, one of which is based on selecting the best crossover operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) have been conducted to evaluate the proposed methods, which are compared to the well-known Modified crossover operator and partially mapped Crossover (PMX) crossover. The results show the importance of some of the proposed methods, such as the collision crossover, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one crossover operator.