(Enter summary)
Abstract: This paper introduces the notion of using coevolution
to adapt the penalty factors of a fitness function
incorporated in a genetic algorithm for numerical
optimization. The proposed approach produces solutions
even better than those previously reported in the literature
for other (GA-based and mathematical programming)
techniques that have been particularly fine-tuned
using a normally lengthy trial and error process to solve a
certain problem or set of problems. The present technique
is also... (Update)
Context of citations to this paper: More
...and considered this approach as a very promising direction of research on evolutionary optimization. Following this idea, Coello [19, 16] proposed the use of a penalty function of the form: fitness i (X) f i (X) Gamma (coef Theta w 1 viol Theta w 2 ) 27) where f i...
...Wu [18] applied an evolutionary programming model. Deb [19] solved the above problem using a genetic adaptive search (GeneAs) and Coello [20] used a genetic algorithm with self adaptive penalties for handling constraints. With a civilization size of 100 over 200 time steps, the...
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2: Simulated binary crossover for continuous search space
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BibTeX entry: (Update)
Carlos A. Coello Coello. Self-Adaptive Penalties for GA-based Optimization. In Proceedings of the 1999 Congress on Evolutionary Computation, Washington, D.C., July 1999. IEEE. http://citeseer.ist.psu.edu/75427.html More
@inproceedings{ coello99selfadaptive,
author = "Carlos A. Coello Coello",
title = "Self-Adaptive Penalties for {GA}-based Optimization",
booktitle = "Proceedings of the Congress on Evolutionary Computation",
volume = "1",
month = "6-9",
publisher = "IEEE Press",
address = "Mayflower Hotel, Washington D.C., USA",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc Schoenauer and Xin Yao and Ali Zalzala",
isbn = "0-7803-5537-7 (Microfiche)",
pages = "573--580",
year = "1999",
url = "citeseer.ist.psu.edu/75427.html" }
Citations (may not include all citations):
1051
Optimization and Machine Learning (context) - Goldberg, in - 1989
74
Evolutionary Algorithms for Constrained Parameter Optimizati..
- Michalewicz, Schoenauer - 1996
32
Evolutionary algorithms for constrained engineering problems
- Michalewicz, Dasgupta et al. - 1996
17
Numerical Optimization (context) - Michalewicz - 1995
2
Design Best solution found Variables This paper Gen [16] Hom.. (context) - Dasgupta, Michalewicz et al. - 1997
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