CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem
Neural and Evolutionary Computing
2024-01-10 v2
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.
Keywords
Cite
@article{arxiv.2303.12447,
title = {CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem},
author = {Martin Uray and Stefan Wintersteller and Stefan Huber},
journal= {arXiv preprint arXiv:2303.12447},
year = {2024}
}
Comments
This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published and is available online at https://doi.org/10.1007/978-3-031-42171-6_3