# A Survey and Analysis of Evolutionary Operators for Permutations

arXiv: https://arxiv.org/abs/2311.14595 · HTML: https://mlsanity.com/paper/2311.14595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [Cycle Mutation: Evolving Permutations via Cycle Induction](https://mlsanity.com/paper/2205.14125/) (2022)
- [On Fitness Landscape Analysis of Permutation Problems: From Distance Metrics to Mutation Operator Selection](https://mlsanity.com/paper/2208.11188/) (2022)

## Built on by (most cited first, 1 of 1)

- [Empirical Evaluation of No Free Lunch Violations in Permutation-Based Optimization](https://mlsanity.com/paper/2603.03613/) (2026)

## Abstract (arXiv)

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.