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:

    01 January 2000

  • Publication Status:

    Published

  • Publisher

    Morgan Kauffmann

  • 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. (2000). Solving CSPs with evolutionary algorithms using self-adaptive constraint weights. In D. Whitley (Ed.), GECCO-2000 : proceedings of the genetic and evolutionary computation conference, 128-134

Authors

Keywords

evolutionary algorithms; constraint satisfactionproblems (CSPs); self-adaptive;

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