Efficient Genetic Algorithms for Solving Hard Constrained Optimization ProblemsReport as inadecuate




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* Corresponding author 1 CEGELY - Centre de génie électrique de Lyon 2 LEEI - Laboratoire électrotechnique et électronique industrielle 3 Ampère

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 showadvantages 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.

Keywords : constrained optimization methods genetic algorithms niching methods shape optimization methods





Author: Bruno Sareni - Laurent Krähenbühl - Alain Nicolas -

Source: https://hal.archives-ouvertes.fr/



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