Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Twomax To The Ising Model: Easy And Hard Symmetrical Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Bayesian optimization algorithm: from single level to hierarchy
Bayesian optimization algorithm: from single level to hierarchy
Learning computer programs with the bayesian optimization algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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I introduce a generalization of probabilistic modeling and sampling for estimation of distribution algorithms (EDAs), that allows models to contain features, additional level(s) of abstraction defined in terms of the problem's base-level variables. I demonstrate how a simple feature class, variable-position motifs within fixed-length strings, may be exploited by a powerful EDA, the Bayesian optimization algorithm (BOA). Experimental results are presented where motifs are learned autonomously via a simple heuristic. The effectiveness of this feature-based BOA is demonstrated across a range of problems where such motifs are relevant.