New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination

New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination” by R. Santana, A. Mendiburu, and J. A. Lozano, Department of Computer Science and Artificial Intelligence. University of the Basque Country technical report EHU-KZAA-TR:2012-05, Dec. 2012.

Abstract

Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAsâ convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete functions.

BibTeX entry:

@techreport{Santana_et_al:2012i,
   author = {R. Santana and A. Mendiburu and J. A. Lozano},
   title = {New methods for generating populations in Markov network based
	EDAs: Decimation strategies and model-based template
	recombination},
   institution = {Department of Computer Science and Artificial
	Intelligence, University of the Basque Country},
   number = {EHU-KZAA-TR:2012-05},
   month = dec,
   year = {2012},
   url = {http://hdl.handle.net/10810/9180}
}

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