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Abstract: Estimation of distribution algorithms (EDAs) are population-based heuristic search methods that use probabilistic models and which have been successfully applied to continuous optimization problems. When applied to constrained optimization problems, most EDAs (as well as genetic algorithms) handle constraints by penalizing invalid solutions. This paper presents PolyEDA, a new EDA approach that is able to directly consider linear inequality constraints by using Gibbs sampling. Gibbs... (Update)
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BibTeX entry: (Update)
@misc{ grahl-polyeda,
author = "Jörn Grahl and Franz Rothlauf",
title = "PolyEDA: Combining Estimation of Distribution Algorithms and Linear Inequality
Constraints",
url = "citeseer.ist.psu.edu/grahl04polyeda.html" }
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