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    Tweaking a tower of blocks leads to a TMBL: pursuing long term fitness growth in program evolution

    Lewis, T.E. and Magoulas, George D. (2010) Tweaking a tower of blocks leads to a TMBL: pursuing long term fitness growth in program evolution. In: UNSPECIFIED (ed.) IEEE Congress on Evolutionary Computation. New York, USA: Institute of Electrical and Electronics Engineers, pp. 1-8. ISBN 9781424469093.

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    Abstract

    If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more complex problems? We begin to investigate this question by introducing Tweaking Mutation Behaviour Learning (TMBL), a form of evolutionary computation designed to meet this challenge. Whereas Genetic Programming (GP) typically involves creating a large pool of initial solutions and then shuffling them (with crossover and mutation) over relatively few generations, TMBL focuses on the cumulative acquisition of small adaptive mutations over many generations. In particular, we aim to reduce limits on long term fitness growth by encouraging tweaks: changes which affect behaviour without ruining the existing functionality. We use this notion to construct a standard representation for TMBL. We then experimentally compare TMBL against linear GP and tree-based GP and find that TMBL shows strong signs of being more conducive to the long term growth of fitness.

    Metadata

    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Sarah Hall
    Date Deposited: 18 Jul 2013 14:07
    Last Modified: 02 Dec 2016 13:23
    URI: https://eprints.bbk.ac.uk/id/eprint/7729

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