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Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions
[chapter]
2008
Lecture Notes in Computer Science
This paper analyzes the effects of restricting probabilistic models in the hierarchical Bayesian optimization algorithm (hBOA) by defining a distance metric over variables and disallowing dependencies between variables at distances greater than a given threshold. We argue that by using prior problem-specific knowledge, it is often possible to develop a distance metric that closely corresponds to the strength of interactions between variables. This distance metric can then be used to speed up
doi:10.1007/978-3-540-87700-4_42
fatcat:27zrnbk7dzd27iflo7ao7vx4mu