Abstract
The previous chapter argued that using probabilistic models with multivariate interactions is a powerful approach to solving problems of bounded difficulty. The Bayesian optimization algorithm (BOA) combines the idea of using probabilistic models to guide optimization and the methods for learning and sampling Bayesian networks. To learn an adequate decomposition of the problem, BOA builds a Bayesian network for the set of promising solutions. New candidate solutions are generated by sampling the built network.
Keywords
- Bayesian Network
- Candidate Solution
- Hill Climber
- Solution String
- Uniform Crossover
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
About this chapter
Cite this chapter
Pelikan, M. Bayesian Optimization Algorithm. In: Hierarchical Bayesian Optimization Algorithm. Studies in Fuzziness and Soft Computing, vol 170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32373-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-32373-0_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23774-7
Online ISBN: 978-3-540-32373-0
eBook Packages: EngineeringEngineering (R0)