Variation Operators for Grouping Genetic Algorithms: A Review

Octavio Ramos-Figueroa, Marcela Quiroz-Castellanos, Efrén Mezura-Montes, Rupak Kharel

Research output: Contribution to journalReview articlepeer-review

32 Citations (Scopus)

Abstract

Grouping problems are combinatorial optimization problems, most of them NP-hard, related to the partition of a set of items into different groups or clusters. Given their numerous real-world applications, different solution approaches have been presented to deal with the high complexity of NP-hard grouping problems. However, the Grouping Genetic Algorithm (GGA) is one of the most outstanding solution methods. GGA is an extension to the traditional Genetic Algorithm (GA) that uses a representation scheme based on groups and variation operators adapted to work at the groups level. Since its emergence, GGA has been used to address several grouping problems with distinct traits. Therefore, at present, there are different variation operators developed to solve problems with diverse grouping constraints and conditions. This paper presents a review of variation operators included in GGAs solving NP-hard grouping problems. Three classifications are introduced, organizing the variation operators according to the variation-degree, the solutions encoding, and the parameter setting-level, respectively.

Original languageEnglish
Article number100796
Number of pages24
JournalSwarm and Evolutionary Computation
Volume60
Early online date17 Nov 2020
DOIs
Publication statusPublished - 1 Feb 2021
Externally publishedYes

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