Elham Salehi
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.
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Digital Library
- Pelikan, M. Bayesian optimization algorithm: from single level to hierarchy, Ph.D. Thesis, University of Illinois, 2006. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Efficient EDA for large opimization problems via constraining the search space of models
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