SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming

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Abstract

Geometric semantic genetic programming (GSGP) is a successful variant of genetic programming (GP), able to induce a unimodal error surface for all supervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant, the Semantic Learning algorithm with Inflate and deflate Mutations (SLIM_GSGP). SLIM_GSGP retains the essential theoretical characteristics of traditional GSGP, including the induction of a unimodal error surface and introduces a novel geometric semantic mutation, the deflate mutation, which generates smaller offspring than its parents. The study introduces four SLIM_GSGP variants and presents experimental results demonstrating that, across six symbolic regression test problems, SLIM_GSGP consistently evolves models with equal or superior performance on unseen data compared to traditional GSGP and standard GP. These SLIM_GSGP models are significantly smaller than those produced by traditional GSGP and are either smaller or of comparable size to standard GP models. Notably, the compactness of SLIM_GSGP models allows for human interpretation.
Original languageEnglish
Title of host publicationGenetic Programming
Subtitle of host publication27th European Conference, EuroGP 2024, Held as Part of EvoStar 2024 Aberystwyth, UK, April 3–5, 2024 Proceedings
EditorsMario Giacobini, Bing Xue, Luca Manzoni
Place of PublicationCham, Switzerland
PublisherSpringer Nature Switzerland AG
Pages125-141
Number of pages17
ISBN (Electronic)978-3-031-56957-9
ISBN (Print)978-3-031-56956-2
DOIs
Publication statusPublished - 28 Mar 2024
Event27th European Conference on Genetic Programming, held as part of EvoStar 2024 - Aberystwyth University, Aberystwyth, United Kingdom
Duration: 3 Apr 20245 Apr 2024
Conference number: 27
https://www.evostar.org/2024/eurogp/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature Switzerland AG
Volume14631
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th European Conference on Genetic Programming, held as part of EvoStar 2024
Abbreviated titleEuroGP 2024
Country/TerritoryUnited Kingdom
CityAberystwyth
Period3/04/245/04/24
Internet address

Keywords

  • Genetic Programming
  • Geometric Semantic Genetic Programming
  • Inflate and Deflate Mutations
  • Model Interpretability

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