Memory with memory in tree-based genetic programming

Riccardo Poli, Nicholas F. McPhee, Luca Citi, Ellery Crane

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    In recent work on linear register-based genetic programming (GP) we introduced the notion ofMemory-with-Memory (MwM), where the results of operations are stored in registers using a form of soft assignment which blends a result into the current content of a register rather than entirely replace it. The MwM system yielded very promising results on a set of symbolic regression problems. In this paper, we propose a way of introducing MwM style behaviour in treebased GP systems. The technique requires only very minor modifications to existing code, and, therefore, is easy to apply. Experiments on a variety of synthetic and real-world problems show that MwM is very beneficial in tree-based GP, too.

    Original languageEnglish (US)
    Title of host publicationGenetic Programming - 12th European Conference, EuroGP 2009, Proceedings
    Pages25-36
    Number of pages12
    DOIs
    StatePublished - Jul 23 2009
    Event12th European Conference on Genetic Programming, EuroGP 2009 - Tubingen, Germany
    Duration: Apr 15 2009Apr 17 2009

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5481 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference12th European Conference on Genetic Programming, EuroGP 2009
    CountryGermany
    CityTubingen
    Period4/15/094/17/09

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  • Cite this

    Poli, R., McPhee, N. F., Citi, L., & Crane, E. (2009). Memory with memory in tree-based genetic programming. In Genetic Programming - 12th European Conference, EuroGP 2009, Proceedings (pp. 25-36). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5481 LNCS). https://doi.org/10.1007/978-3-642-01181-8_3