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Message Passing Methods for Estimation of Distribution Algorithms Based on Markov Networks

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2013)

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Abstract

Sampling methods are a fundamental component of estimation of distribution algorithms (EDAs). In this paper we propose new methods for generating solutions in EDAs based on Markov networks. These methods are based on the combination of message passing algorithms with decimation techniques for computing the maximum a posteriori solution of a probabilistic graphical model. The performance of the EDAs on a family of non-binary deceptive functions shows that the introduced approach improves results achieved with the sampling methods traditionally used by EDAs based on Markov networks.

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Santana, R., Mendiburu, A., Lozano, J.A. (2013). Message Passing Methods for Estimation of Distribution Algorithms Based on Markov Networks. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-03756-1_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03755-4

  • Online ISBN: 978-3-319-03756-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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