Offspring selection genetic algorithm revisited: Improvements in efficiency by early stopping criteria in the evaluation of unsuccessful individuals

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Abstract

This paper proposes some algorithmic extensions to the general concept of offspring selection which itself is an algorithmic extension of genetic algorithms and genetic programming. Offspring selection is characterized by the fact that many offspring solution candidates will not participate in the ongoing evolutionary process if they do not achieve the success criterion. The algorithmic enhancements proposed in this contribution aim to early estimate if a solution candidate will not be accepted based on partial solution evaluation. The qualitative characteristics of offspring selection are not affected by this means. The discussed variant of offspring selection is analyzed for several symbolic regression problems with offspring selection genetic programming. The achievable gains in terms of efficiency are remarkable especially for large data-sets.

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
PublisherSpringer Verlag
Pages424-431
Number of pages8
ISBN (Print)9783319747170
DOIs
Publication statusPublished - 2018
Event16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain
Duration: 19 Feb 201724 Feb 2017

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

Conference16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
CountrySpain
CityLas Palmas de Gran Canaria
Period19.02.201724.02.2017

Keywords

  • Symbolic Regression

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