CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem

TL;DR

This paper introduces a new crossover operator for genetic algorithms that leverages symmetries in the solution space to improve performance on the Traveling Salesperson Problem.

Contribution

A family of novel crossover operators exploiting solution symmetries, enhancing genetic algorithm effectiveness for TSP beyond previous methods.

Findings

Outperforms existing crossover methods on TSP benchmarks

Effectively preserves high-quality solutions through symmetry exploitation

Demonstrates general applicability beyond TSP

Abstract

In this paper, we revisit the application of Genetic Algorithm (GA) to the Traveling Salesperson Problem (TSP) and introduce a family of novel crossover operators that outperform the previous state of the art. The novel crossover operators aim to exploit symmetries in the solution space, which allows us to more effectively preserve well-performing individuals, namely the fitness invariance to circular shifts and reversals of solutions. These symmetries are general and not limited to or tailored to TSP specifically.