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.
- Bayesian Network
- Candidate Solution
- Hill Climber
- Solution String
- Uniform Crossover
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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
Publisher Name: Springer, Berlin, Heidelberg
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