Analysis of Schema Frequencies in Genetic Programming

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Abstract

Genetic Programming (GP) schemas are structural templates equivalent to hyperplanes in the search space. Schema theories provide information about the properties of subsets of the population and the behavior of genetic operators. In this paper we propose a practical methodology to identify relevant schemas and measure their frequency in the population. We demonstrate our approach on an artificial symbolic regression benchmark where the parts of the formula are already known. Experimental results reveal how solutions are assembled within GP and explain diversity loss in GP populations through the proliferation of repeated patterns.
Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
EditorsRoberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Pages432-438
Number of pages7
DOIs
Publication statusPublished - 2018
EventInternational Conference Computer Aided Systems Theory EUROCAST 2017 - Las Palmas de Gran Canaria, Spain
Duration: 19 Feb 201724 Feb 2017
http://eurocast2017.fulp.ulpgc.es/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10671 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference Computer Aided Systems Theory EUROCAST 2017
CountrySpain
CityLas Palmas de Gran Canaria
Period19.02.201724.02.2017
Internet address

Keywords

  • genetic programming
  • schema analysis
  • symbolic regression
  • tree pattern matching
  • evolutionary dynamics
  • loss of diversity
  • Schema analysis
  • Symbolic regression
  • Evolutionary dynamics
  • Genetic programming
  • Tree pattern matching
  • Loss of diversity

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