# 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.

Download#### Paper Citation

#### 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