CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866913189210357760 |
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| author | Uray, Martin Wintersteller, Stefan Huber, Stefan |
| author_facet | Uray, Martin Wintersteller, Stefan Huber, Stefan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12447 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem Uray, Martin Wintersteller, Stefan Huber, Stefan Neural and Evolutionary Computing 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. |
| title | CSRX: A novel Crossover Operator for a Genetic Algorithm applied to the Traveling Salesperson Problem |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2303.12447 |