Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions [chapter]

Mark Hauschild, Martin Pelikan
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
more » ... el building in hBOA. Three test problems are considered: 3D Ising spin glasses, random additively decomposable problems, and the minimum vertex cover. Keywords Hierarchical BOA, efficiency enhancement, learning from experience, probabilistic model, model structure, model complexity, estimation of distribution algorithms. Abstract 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 model building in hBOA. Three test problems are considered: 3D Ising spin glasses, random additively decomposable problems, and the minimum vertex cover.
doi:10.1007/978-3-540-87700-4_42 fatcat:27zrnbk7dzd27iflo7ao7vx4mu