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    Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression

    Nguyen, Quang Uy and O'Neill, Michael and Xuan Hoai, Nguyen and McKay, Bob and Galvan, Edgar (2009) Semantic Aware Crossover for Genetic Programming: The Case for Real-Valued Function Regression. Lecture Notes in Computer Science, 5481. pp. 292-302. ISSN 0302-9743

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    In this paper, we apply the ideas from [2] to investigate the effect of some semantic based guidance to the crossover operator of GP. We conduct a series of experiments on a family of real-valued symbolic regression problems, examining four different semantic aware crossover operators. One operator considers the semantics of the exchanged subtrees, while the other compares the semantics of the child trees to their parents. Two control operators are adopted which reverse the logic of the semantic equivalence test. The results show that on the family of test problems examined, the (approximate) semantic aware crossover operators can provide performance advantages over the standard subtree crossover adopted in Genetic Programming.

    Item Type: Article
    Keywords: Semantic Similarity; Based Crossover; GP; Real-Valued; Function; Regression;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15427
    Identification Number:
    Depositing User: Edgar Galvan
    Date Deposited: 07 Feb 2022 15:52
    Journal or Publication Title: Lecture Notes in Computer Science
    Publisher: Springer Verlag
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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