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Published Articles >> Table of Contents >> Abstract
Sixth Mexican International Conference on Computer Science (ENC'05)
pp. 274-281
Saving Evaluations in Differential Evolution for Constrained Optimization
Efren Mezura-Montes, (EVOCINV) at CINVESTAV-IPN, Mexico
Carlos A. Coello Coello, (EVOCINV) at CINVESTAV-IPN, Mexico
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ENC.2005.38
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| Abstract |
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Generally, evolutionary algorithms require a large number
of evaluations of the objective function in order to obtain
a good solution. This paper presents a simple approach
to save evaluations, applied to a competitive differential
evolution algorithm used to solve constrained optimization
problems. The idea is based on the way in which differential
evolution finds new promising areas of the search space.
This allows to randomly assign a zero fitness to some offspring
newly generated in order to avoid its evaluation and,
as a secondary effect, to slow down convergence. The approach
is tested using different percentages of individuals
from the population, providing a competitive performance.
Besides, the effect that the elimination of individuals has on
convergence is also analyzed. Finally, to remark behavior
differences, the approach is tested against a version with a
smaller population and against a version with a simple fitness
approximation method. The results obtained are discussed
and some conclusions are drawn.
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 Additional Information
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Citation:
Efren Mezura-Montes, Carlos A. Coello Coello,
"Saving Evaluations in Differential Evolution for Constrained Optimization,"
enc,
pp. 274-281,
Sixth Mexican International Conference on Computer Science (ENC'05),
2005
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