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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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 14:12
Last Modified: 17 Mar 2014 11:48

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