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

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

5 Zitate (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.
OriginalspracheEnglisch
Titel2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Herausgeber (Verlag)IEEE Computational Intelligence Society
Seiten2175-2182
Seitenumfang8
ISBN (elektronisch)9781728121536
ISBN (Print)1089-778X
DOIs
PublikationsstatusVeröffentlicht - Jun 2019
VeranstaltungIEEE Congress on Evolutionary Computation - Wellington, New Zealand, Australien
Dauer: 10 Jun 201913 Jun 2019
http://cec2019.org/index.html

Publikationsreihe

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

Konferenz

KonferenzIEEE Congress on Evolutionary Computation
Land/GebietAustralien
OrtWellington, New Zealand
Zeitraum10.06.201913.06.2019
Internetadresse

Schlagwörter

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

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