Entropy-based evaluation relaxation strategy for Bayesian optimization algorithm

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 real-world optimization, BOA needs to be coupled with various efficiency enhancement techniques. A BOA incorporated with a novel entropy-based 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. © 2010 Springer-Verlag.