Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

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4 Citations (Scopus)

Abstract

Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm’s runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.
Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherIEEE Computational Intelligence Society
Pages2175-2182
Number of pages8
ISBN (Electronic)9781728121536
ISBN (Print)1089-778X
DOIs
Publication statusPublished - Jun 2019
EventIEEE Congress on Evolutionary Computation - Wellington, New Zealand, Australia
Duration: 10 Jun 201913 Jun 2019
http://cec2019.org/index.html

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Conference

ConferenceIEEE Congress on Evolutionary Computation
CountryAustralia
CityWellington, New Zealand
Period10.06.201913.06.2019
Internet address

Keywords

  • symbolic regression
  • genetic programming
  • multi-objective symbolic regression
  • tree distance
  • genetic diversity
  • population diversity
  • tree hash
  • multi-objective

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