- Generating Lambda Term Individuals in Typed Genetic Programming Usin…
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Generating Lambda Term Individuals in Typed Genetic Programming Using Forgetful A*

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    SYSNO ASEP0435886
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleGenerating Lambda Term Individuals in Typed Genetic Programming Using Forgetful A*
    Author(s) Křen, T. (CZ)
    Neruda, Roman (UIVT-O) SAI, RID, ORCID
    Source Title2014 IEEE Congress on Evolutionary Computation. - Piscataway : IEEE CS, 2014 - ISBN 978-1-4799-6626-4
    Pagess. 1847-1854
    Number of pages8 s.
    Publication formPrint - P
    ActionCEC 2014. IEEE Congress on Evolutionary Computation
    Event date06.06.2014-11.06.2014
    VEvent locationBeijing
    CountryCN - China
    Event typeWRD
    Languageeng - English
    CountryUS - United States
    Keywordsgenetic algorithms ; geometry ; lambda calculus ; search problems
    Subject RIVIN - Informatics, Computer Science
    R&D ProjectsGAP202/10/1333 GA ČR - Czech Science Foundation (CSF)
    LD13002 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    Institutional supportUIVT-O - RVO:67985807
    UT WOS000356684602069
    EID SCOPUS84908577177
    DOI https://doi.org/10.1109/CEC.2014.6900547
    AnnotationTree based genetic programming (GP) traditionally uses simple S-expressions to represent programs, however more expressive representations, such as lambda calculus, can exhibit better results while being better suited for typed GP. In this paper we present population initialization methods within a framework of GP over simply typed lambda calculus that can be also used in the standard GP approach. Initializations can be parameterized by different search strategies, leading to wide spectrum of methods corresponding to standard ramped halfand- half initialization on one hand, or exhaustive systematic search on the other. A novel geometric strategy is proposed that balances those two approaches. Experiments on well known benchmark problems show that the geometric strategy outperforms the standard generating method in success rate, best fitness value, time consumption and average individual size.
    WorkplaceInstitute of Computer Science
    ContactTereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
    Year of Publishing2015
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