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Solving CSPs with evolutionary algorithms using self-adaptive constraint weights.

Eiben, A E, Jansen, B, Michalewicz, Z and Paechter, Ben (2000) Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In: Genetic and Evolutionary Computation Conference - GECCO 2000. Amercian Association for Artificial Intelligence, pp. 128-134.

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    Abstract/Description

    This paper examines evolutionary algorithms
    (EAs) extended by various penalty-based
    approaches to solve constraint satisfaction
    problems (CSPs). In some approaches, the
    penalties are set in advance and they do not
    change during a run. In other approaches,
    dynamic or adaptive penalties that change
    during a run according to some mechanism
    (a heuristic rule or a feedback), are used. In
    this work we experimented with self-adaptive
    approach, where the penalties change during
    the execution of the algorithm, however, no
    feedback mechanism is used. The penalties
    are incorporated in the individuals and evolve
    together with the solutions

    Item Type: Book Section
    Uncontrolled Keywords: evolutionary algorithms; constraint satisfaction problems (CSPs); self-adaptive;
    University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
    Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods > 006.3 Artificial intelligence
    Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Item ID: 3198
    Depositing User: Computing Research
    Date Deposited: 02 Aug 2010 15:12
    Last Modified: 17 Mar 2014 11:48
    URI: http://researchrepository.napier.ac.uk/id/eprint/3198

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