On the success rate of crossover operators for genetic programming with offspring selection

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Abstract

Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve amongst others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers
Pages793-800
Number of pages8
Volume5717
Edition2009
DOIs
Publication statusPublished - 2009
Event12th International Conference on Computer Aided Systems Theory, EUROCAST 2009 - Las Palmas de Gran Canaria, Spain
Duration: 15 Feb 200920 Feb 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5717 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Computer Aided Systems Theory, EUROCAST 2009
CountrySpain
CityLas Palmas de Gran Canaria
Period15.02.200920.02.2009

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