Entropy measurement-based estimation model for bayesian optimization algorithm

Authors:
Hai T.T. Nguyen;Hoang N. Luong;Chang Wook Ahn
Affiliations:
Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea;Sungkyunkwan University, Suwon, South Korea
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

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Abstract

In evolutionary algorithms, the efficiency enhancement techniques are capable of solving difficult large scale problems in a scalable manner. This paper rigorously analyzes the Bayesian optimization algorithm (BOA) incorporated with an innovative evaluation relaxation method based on the entropy measurement theory (en-BOA). In particular, the concept of entropy is used to develop the evaluation relaxation strategy (ERS) and to determine the rate of convergence. Entropy measurement-based ERS is employed to recognize which candidate solution should be evaluated by the actual function or be estimated by the surrogate model. Experiments prove that en-BOA significantly reduces the number of actual evaluations and the scalability of BOA is not negatively affected. Moreover, the entropy measurement-based evaluation relaxation technique does not require any larger population sizes.