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

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

1 Zitat (Scopus)

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.

OriginalspracheEnglisch
TitelComputer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers
Seiten793-800
Seitenumfang8
Band5717
Auflage2009
DOIs
PublikationsstatusVeröffentlicht - 2009
Veranstaltung12th International Conference on Computer Aided Systems Theory, EUROCAST 2009 - Las Palmas de Gran Canaria, Spanien
Dauer: 15 Feb 200920 Feb 2009

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band5717 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz12th International Conference on Computer Aided Systems Theory, EUROCAST 2009
Land/GebietSpanien
OrtLas Palmas de Gran Canaria
Zeitraum15.02.200920.02.2009

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