On the evolutionary behavior of genetic programming with constants optimization

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Abstract

Evolutionary systems are characterized by two seemingly contradictory properties: robustness and evolvability. Robustness is generally defined as an organism's ability to withstand genetic perturbation while maintaining its phenotype. Evolvability, as an organism's ability to produce useful variation. In genetic programming, the relationship between the two, mediated by selection and variation-producing operators (recombination and mutation), makes it difficult to understand the behavior and evolutionary dynamics of the search process. In this paper, we show that a local gradient-based constants optimization step can improve the overall population evolvability by inducing a beneficial structure-preserving bias on selection, which in the long term helps the process maintain diversity and produce better solutions.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2013 - 14th International Conference, Revised Selected Papers
PublisherSpringer
Pages284-291
Number of pages8
EditionPART 1
ISBN (Print)9783642538551
DOIs
Publication statusPublished - 2013
Event14th International Conference on Computer Aided Systems Theory, Eurocast 2013 - Las Palmas de Gran Canaria, Spain
Duration: 10 Feb 201315 Feb 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8111 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Computer Aided Systems Theory, Eurocast 2013
CountrySpain
CityLas Palmas de Gran Canaria
Period10.02.201315.02.2013

Keywords

  • Algorithm Analysis
  • Constant Optimization
  • Evolutionary Behavior
  • Genetic Programming
  • Symbolic Regression

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