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Efficient EDA for large opimization problems via constraining the search space of models

Published: 12 July 2011 Publication History

Abstract

Introducing efficient Bayesian learning algorithms in Bayesian network based EDAs seems necessary in order to use them for large problems. In this paper we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) [3] in order to constraint the models search space. This algorithm does not consider a fix and small upper bound on the order of interaction between variables and is able solve problems with large number of variables efficiently. We compare the efficiency of CMSS-BOA with standard Bayesian network based EDA for solving several benchmark problems.

References

[1]
Larranaga, P. and Lozano, J. A. Estimation of Distribution Algorithms. Kluwer Academic publisher, 2002.
[2]
Pelikan, M. Bayesian optimization algorithm: from single level to hierarchy, Ph.D. Thesis, University of Illinois, 2006.
[3]
Tsamardinos, I., Brown, L. E., Aliferis, C. F. The MMPC hill-climbing Bayesian network structure learning algorithm, Machine Learning Journal, 65(1):31--78, 2006.

Cited By

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  • (2019)Estimation of Distribution using Population Queue based Variational Autoencoders2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790077(1406-1414)Online publication date: Jun-2019
  • (2012)The Emergence of New Genes in EcoSim and Its Effect on FitnessSimulated Evolution and Learning10.1007/978-3-642-34859-4_6(52-61)Online publication date: 2012

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  1. Efficient EDA for large opimization problems via constraining the search space of models

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      cover image ACM Conferences
      GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
      July 2011
      1548 pages
      ISBN:9781450306904
      DOI:10.1145/2001858

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 12 July 2011

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      Author Tags

      1. bayesian optimization algorithm
      2. estimation of distribution algorithms
      3. optimization
      4. probabilistic models

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      • (2019)Estimation of Distribution using Population Queue based Variational Autoencoders2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790077(1406-1414)Online publication date: Jun-2019
      • (2012)The Emergence of New Genes in EcoSim and Its Effect on FitnessSimulated Evolution and Learning10.1007/978-3-642-34859-4_6(52-61)Online publication date: 2012

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