Dealing with constraints with estimation of distribution algorithms: The univariate case

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“Dealing with constraints with estimation of distribution algorithms: The univariate case” by R. Santana and A. Ochoa. In Proceedings of the Second Symposium on Artificial Intelligence (CIMAF-99), (A. Ochoa, M. R. Soto, and R. Santana, eds.), (Havana, Cuba), Mar. 1999, pp. 378-384.

Abstract

This paper proposes a new optimization algorithm to deal with binary constraint problems. The algorithm is based in the Univariate Marginal Distribution Algorithm. We apply our approach to the optimization of functions with different characteristics. For the test functions considered we show the superiority of our algorithm to traditional population search methods that have been used to solve these kind of problems. We report some particular features exhibited by the algorithm and discuss extensions that could make of it a still more powerful optimization tool.

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BibTeX entry:

@inproceedings{Santana_and_Ochoa:1999b,
   author = {R. Santana and A. Ochoa},
   editor = {A. Ochoa and M. R. Soto and R. Santana},
   title = {Dealing with constraints with estimation of distribution
	algorithms: The univariate case},
   booktitle = {Proceedings of the Second Symposium on Artificial
	Intelligence (CIMAF-99)},
   pages = {378-384},
   address = {Havana, Cuba},
   month = mar,
   year = {1999}
}

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