Research Output

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

  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

  • Date:

    30 November 1999

  • Publication Status:

    Published

  • Publisher

    Amercian Association for Artificial Intelligence

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

Citation

Eiben, A. E., Jansen, B., Michalewicz, Z. & Paechter, B. (1999). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In Genetic and Evolutionary Computation Conference - GECCO 2000, 128-134

Authors

Keywords

evolutionary algorithms; constraint satisfaction problems (CSPs); self-adaptive;

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