Schema-based Diversification in Genetic Programming

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Abstract

In genetic programming (GP), population diversity represents a key aspect of evolutionary search and a major factor in algorithm performance. In this paper we propose a new schema-based approach for observing and steering the loss of diversity in GP populations. We employ a well-known hyperschema definition from the literature to generate tree structural templates from the population's genealogy, and use them to guide the search via localized mutation within groups of individuals matching the same schema. The approach depends only on genealogy information and is easily integrated with existing GP variants. We demonstrate its potential in combination with Offspring Selection GP (OSGP) on a series of symbolic regression benchmark problems where our algorithmic variant called OSGP-S obtains superior results.
Original languageEnglish
Title of host publicationGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
PublisherACM Press
Pages1111-1118
Number of pages8
ISBN (Electronic)9781450356183
DOIs
Publication statusPublished - 2 Jul 2018
EventGenetic and Evolutionary Computation Conference (GECCO 2018) - Kyoto, Japan, Japan
Duration: 15 Jul 201819 Jul 2018
http://gecco-2018.sigevo.org/

Publication series

NameGECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference

Conference

ConferenceGenetic and Evolutionary Computation Conference (GECCO 2018)
CountryJapan
CityKyoto, Japan
Period15.07.201819.07.2018
Internet address

Keywords

  • Diversity
  • Genealogy
  • Genetic programming
  • Pattern matching
  • Schema analysis

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