Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis
- Angel Fernando Kuri-MoralesAffiliated withInstituto Tecnológico Autónomo de México
- , Jesús Gutiérrez-GarcíaAffiliated withCentro de Investigación en Computación, Instituto Politécnico Nacional
Genetic algorithms (GAs) have been successfully applied to numerical optimization problems. Since GAs are usually designed for unconstrained optimization, they have to be adapted to tackle the constrained cases, i.e. those in which not all representable solutions are valid. In this work we experimentally compare 5 ways to attain such adaptation. Our analysis relies on the usual method of selecting an arbitrary suite of test functions (25 of these) albeit applying a methodology which allows us to determine which method is better within statistical certainty limits. In order to do this we have selected 5 penalty function strategies; for each of these we have further selected 3 particular GAs. The behavior of each strategy and the associated GAs is then established by extensively sampling the function suite and finding the worst case best values from Chebyshev’s theorem. We have found some counter- intuitive results which we discuss and try to explain.
- Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis
- Book Title
- MICAI 2002: Advances in Artificial Intelligence
- Book Subtitle
- Second Mexican International Conference on Artificial Intelligence Mérida, Yucatán, Mexico, April 22–26, 2002 Proceedings
- pp 108-117
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Computer Science Section, Electrical Engineering Department, CINVESTAV-IPN
- 2. Computer Science Department, ITESM-Mexico City
- 3. Computer Science Department, ITESM-Cuernavaca
- 4. Department of Computer Science, ITAM
- Author Affiliations
- 5. Instituto Tecnológico Autónomo de México, Río Hondo No. 1, México D.F.
- 6. Centro de Investigación en Computación, Instituto Politécnico Nacional, México D.F.
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