On the genotype compression and expansion for evolutionary algorithms in the continuous domain

Lucija Planinic, Marko Djurasevic, Luca Mariot, Domagoj Jakobovic, Stjepan Picek, Carlos Coello Coello

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

This paper investigates the influence of genotype size on evolutionary algorithms' performance. We consider genotype compression (where genotype is smaller than phenotype) and expansion (genotype is larger than phenotype) and define different strategies to reconstruct the original variables of the phenotype from both the compressed and expanded genotypes. We test our approach with several evolutionary algorithms over three sets of optimization problems: COCO benchmark functions, modeling of Physical Unclonable Functions, and neural network weight optimization. Our results show that genotype expansion works significantly better than compression, and in many scenarios, outperforms the original genotype encoding. This could be attributed to the change in the genotype-phenotype mapping introduced with the expansion methods: this modification beneficially transforms the domain landscape and alleviates the search space traversal.

Original languageEnglish
Title of host publicationGECCO 2021 Companion
Subtitle of host publicationProceedings of the 2021 Genetic and Evolutionary Computation Conference Companion
EditorsE. Chicano, K. Krawiec
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1208-1216
Number of pages9
ISBN (Print)978-1-4503-8351-6
DOIs
Publication statusPublished - 2021
Event2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France
Duration: 10 Jul 202114 Jul 2021

Conference

Conference2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Country/TerritoryFrance
CityVirtual, Online
Period10/07/2114/07/21

Keywords

  • compression
  • expansion
  • genotype
  • phenotype

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