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.
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Luong, H.N., Nguyen, H.T.T., Ahn, C.W. (2010). Entropy-Based Evaluation Relaxation Strategy for Bayesian Optimization Algorithm. In: GarcÃa-Pedrajas, N., Herrera, F., Fyfe, C., BenÃtez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_14
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DOI: https://doi.org/10.1007/978-3-642-13025-0_14
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