A Survey and Analysis of Evolutionary Operators for Permutations
Vincent Cicirello
2023
Abstract
There are many combinatorial optimization problems whose solutions are best represented by permutations. The classic traveling salesperson seeks an optimal ordering over a set of cities. Scheduling problems often seek optimal orderings of tasks or activities. Although some evolutionary approaches to such problems utilize the bit strings of a genetic algorithm, it is more common to directly represent solutions with permutations. Evolving permutations directly requires specialized evolutionary operators. Over the years, many crossover and mutation operators have been developed for solving permutation problems with evolutionary algorithms. In this paper, we survey the breadth of evolutionary operators for permutations. We implemented all of these in Chips-n-Salsa, an open source Java library for evolutionary computation. Finally, we empirically analyze the crossover operators on artificial fitness landscapes isolating different permutation features.
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in Harvard Style
Cicirello V. (2023). A Survey and Analysis of Evolutionary Operators for Permutations. In Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-674-3, SciTePress, pages 288-299. DOI: 10.5220/0012204900003595
in Bibtex Style
@conference{ecta23,
author={Vincent Cicirello},
title={A Survey and Analysis of Evolutionary Operators for Permutations},
booktitle={Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2023},
pages={288-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012204900003595},
isbn={978-989-758-674-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 15th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - A Survey and Analysis of Evolutionary Operators for Permutations
SN - 978-989-758-674-3
AU - Cicirello V.
PY - 2023
SP - 288
EP - 299
DO - 10.5220/0012204900003595
PB - SciTePress