Entropy-based evaluation relaxation strategy for Bayesian optimization algorithm
- Type
- Conference Paper
- Citation
- 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2010, pp.126 - 135
- Issued Date
- 2010-06
- 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.
- Publisher
- IEA/AIE
- Conference Place
- SP
- Appears in Collections:
- Department of AI > 2. Conference Papers
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