On Fitness Landscape Analysis of Permutation Problems: From Distance Metrics to Mutation Operator Selection

Cicirello, Vincent A.

doi:10.1007/s11036-022-02060-z

Cited by 4 publications

(11 citation statements)

References 23 publications

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“…In Section 5, we perform fitness landscape analysis of the crossover operators on artificial landscapes that isolate different permutation features. This complements our prior analysis of mutation operators (Cicirello, 2023). We discuss conclusions in Section 6.…”

Section: Introductionsupporting

confidence: 62%

“…Many of these, however, are commonly utilized without specific attribution, and their origins likely lost to history. If additional reference is needed, there are many studies to consult (Bossek et al, 2023;Cicirello, 2023;Sutton et al, 2014;Serpell and Smith, 2010;Eiben and Smith, 2003;Valenzuela, 2001). Swap: Swap mutation (also known as exchange) chooses two different elements uniformly at random and swaps them.…”

Section: Mutation Operatorsmentioning

confidence: 99%

“…Adjacent Swap: This is swap mutation restricted to adjacent elements. It tends to be associated with very slow search progress (Cicirello, 2023) due to a very small neighborhood size compared to other available mutation operators, and is thus of more limited application. Its runtime is O(1).…”

Section: Mutation Operatorsmentioning

confidence: 99%

“…Operators like Cycle Crossover (Oliver et al, 1987) enable children to inherit element positions from parents. Several studies examine the variety of problem features that can be important to solution fitness for permutation problems (Campos et al, 2005;Cicirello, 2019;Cicirello, 2023). This is why the literature is so rich with crossover and mutation operators for permutations.…”

Section: Introductionmentioning

confidence: 99%

“…We analyze the operational behavior of the crossover operators in this paper, including a fitness landscape analysis in Section 5. For the mutation operators, some of the insights come from our prior work on fitness landscape analysis (Cicirello, 2023). The permutation features that we consider are as follows:…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey and Analysis of Evolutionary Operators for Permutations

Cicirello

2023

Proceedings of the 15th International Joint Conference on Computational Intelligence

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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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Section: Introductionsupporting

confidence: 62%