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Efficient genetic algorithms for solving hard constrained optimization problems

Sareni, Bruno and Krähenbühl, Laurent and Nicolas, Alain Efficient genetic algorithms for solving hard constrained optimization problems. (2000) IEEE Transactions on Magnetics, vol. 36 (n° 4). pp. 1027-1030. ISSN 0018-9464

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Official URL: http://dx.doi.org/10.1109/20.877616

Abstract

This paper studies many Genetic Algorithm strategies to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to show advantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation method

Item Type:Article
Additional Information:Thanks to IEEE editor. The original publication is available at http://ieeexplore.ieee.org
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - INPT (FRANCE)
Other partners > Institut National des Sciences Appliquées de Lyon - INSA (FRANCE)
Other partners > Université Claude Bernard-Lyon I - UCBL (FRANCE)
Other partners > Ecole Centrale de Lyon (FRANCE)
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Deposited By: Bruno SARENI
Deposited On:20 Dec 2012 09:42

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