Bayesian Networks Model for Estimation of Distribution Algorithms

Cai Chang Ding; Wen Xiu Peng; Wei Ming Wang

doi:10.4028/www.scientific.net/AMR.926-930.3594

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A New Clustering Algorithm and Related Quantitative Descriptions in Ad Hoc Networks
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 926-930Bayesian Networks Model for Estimation of...

Bayesian Networks Model for Estimation of Distribution Algorithms

Abstract:

Estimation of Distribution Algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. In EDAs there are neither crossover nor mutation operators. Instead, the new population of individuals is sampled from a probability distribution, which is estimated from a database that contains the selected individuals from the previous generation. Thus, the interrelations between the different variables that represent the individuals may be explicitly expressed through the joint probability distribution associated with the individuals selected at each generation.

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Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3594-3597

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.926-930.3594

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Online since:

May 2014

Authors:

Cai Chang Ding*, Wen Xiu Peng, Wei Ming Wang

Keywords:

Bayesian Network (BN), Factorize, Probability Distribution, Sample

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