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Self-Adaptive Penalties for GA-based Optimization (1999)  (Make Corrections)  (3 citations)
Carlos A. Coello Coello
Proceedings of the Congress on Evolutionary Computation



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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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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" }
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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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