Improving the generalisation ability of genetic programming with semantic similarity based crossover

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Title: Improving the generalisation ability of genetic programming with semantic similarity based crossover
Authors: Nguyen, Quang Uy
Nguyen, Thi Hien
Nguyen, Xuan Hoai
O'Neill, Michael
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Date: 2010
Online since: 2010-11-22T14:38:12Z
Abstract: This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets is also investigated for both standard crossover and SSC. All GP systems are tested on a number of real-valued symbolic regression problems. The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing data. Further recorded statistics shows that the size of the evolved solutions by using SSC are often smaller than ones obtained from GP systems that do not use semantics. This can be seen as one of the reasons for the success of SSC in improving the generalisation ability of GP.
Funding Details: Irish Research Council for Science, Engineering and Technology
Type of material: Conference Publication
Publisher: Springer
Copyright (published version): 2010 Springer-Verlag Berlin Heidelberg
Keywords: SemanticsGenetic programmingEvolutionary computationNatural computingGeneralisationCrossover
Subject LCSH: Genetic programming (Computer science)
Semantic computing
Natural computation
Evolutionary computation
DOI: 10.1007/978-3-642-12148-7_16
Other versions: The final publication is available at
Language: en
Status of Item: Peer reviewed
Is part of: Esparcia-Alcázar, A. I. et al. (eds.). Genetic Programming 13th European Conference, EuroGP 2010, Istanbul, Turkey, April 7-9, 2010. Proceedings
Conference Details: European Conference on Genetic Programming, Istanbul Turkey, 7-9 April 2010
ISBN: 978-3-642-12147-0
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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