A Framework for Estimation of Distribution Algorithms Based on Maximum Entropy

Author

Jiang, Qun ; Wang, Yue ; Yang, Xiao Qing

Author_Institution

Coll. of Comput. Sci., Chongqing Univ. of Technol., Chongqing, China

Volume

1

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

7

Lastpage

11

Abstract

A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms achieve more stable performance and stronger ability in searching the global optima.

Keywords

Bayes methods; maximum entropy methods; optimisation; Bayesian optimization algorithm; estimation of distribution algorithms; maximum entropy; structure learning; Bayesian methods; Computational efficiency; Computer science; Distributed computing; Educational institutions; Electronic design automation and methodology; Entropy; Frequency estimation; Probability distribution; Uncertainty; Constrain; Estimation of Distribution Algorithms; Probability Distribution; Schema;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.208

Filename

5363282