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Erschienen in:
The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Automatic Derivation of Search Objectives for Test-Based Genetic Programming

verfasst von : Krzysztof Krawiec, Paweł Liskowski

Erschienen in: Genetic Programming

Verlag: Springer International Publishing

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Abstract

In genetic programming (GP), programs are usually evaluated by applying them to tests, and fitness function indicates only how many of them have been passed. We posit that scrutinizing the outcomes of programs’ interactions with individual tests may help making program synthesis more effective. To this aim, we propose DOC, a method that autonomously derives new search objectives by clustering the outcomes of interactions between programs in the population and the tests. The derived objectives are subsequently used to drive the selection process in a single- or multiobjective fashion. An extensive experimental assessment on \(15\) discrete program synthesis tasks representing two domains shows that DOC significantly outperforms conventional GP and implicit fitness sharing.

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Vorheriges Kapitel On the Generalization Ability of Geometric Semantic Genetic Programming
Nächstes Kapitel Evolutionary Design of Transistor Level Digital Circuits Using Discrete Simulation
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Metadaten
Titel
Automatic Derivation of Search Objectives for Test-Based Genetic Programming
verfasst von
Krzysztof Krawiec
Paweł Liskowski
Copyright-Jahr
2015
Buch
Genetic Programming

Print ISBN: 978-3-319-16500-4

Electronic ISBN: 978-3-319-16501-1

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-16501-1_5

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