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.
Publié le : 2000-07-05
Classification:
shape optimization methods,
niching methods,
genetic algorithms,
constrained optimization methods,
[SPI.OTHER]Engineering Sciences [physics]/Other,
[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism,
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]
@article{hal-00135911,
author = {Sareni, Bruno and Kr\"ahenb\"uhl, Laurent and Nicolas, Alain},
title = {Efficient Genetic Algorithms for Solving Hard Constrained Optimization Problems},
journal = {HAL},
volume = {2000},
number = {0},
year = {2000},
language = {en},
url = {http://dml.mathdoc.fr/item/hal-00135911}
}
Sareni, Bruno; Krähenbühl, Laurent; Nicolas, Alain. Efficient Genetic Algorithms for Solving Hard Constrained Optimization Problems. HAL, Tome 2000 (2000) no. 0, . http://gdmltest.u-ga.fr/item/hal-00135911/