Entropy-based evaluation relaxation strategy for Bayesian optimization algorithm

Authors:
Hoang Ngoc Luong;Hai Thanh Thi Nguyen;Chang Wook Ahn
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Republic of Korea
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Year:
2010

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Abstract

Bayesian Optimization Algorithm (BOA) belongs to the advanced evolutionary algorithms (EA) capable of solving problems with multivariate interactions. However, to attain wide applicability in realworld optimization, BOA needs to be coupled with various efficiency enhancement techniques. A BOA incorporated with a novel entropybased evaluation relaxation method (eBOA) is developed in this regard. Composed of an on-demand evaluation strategy (ODES) and a sporadic evaluation method, eBOA significantly reduces the number of (fitness) evaluations without imposing any larger population-sizing requirement. Experiments adduce the grounds for its significant improvement in the number of evaluations until reliable convergence. Furthermore, the evaluation relaxation does not negatively affect the scalability performance.