Levels of abstraction in modeling and sampling
Moshe Looks · 2006
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.