Abstract
The intrinsic feature of Estimation of Distribution Algorithms lies in their ability to learn and employ probabilistic models over the input spaces. Discovery of the appropriate model usually implies a computationally expensive comprehensive search, where many models are proposed and evaluated in order to find the best value of some model discriminative scoring metric. This chapter presents how simple pairwise interaction variable data can be extended and used to efficiently guide the model search, decreasing the number of model evaluations by several orders of magnitude or even facilitate the finding of richer, qualitatively better models. As case studies, first the O(n 3) model building of the Extended Compact Genetic Algorithm is successfully replaced by a correlation guided search of linear complexity, which infers the perfect problem structures on the test suites. In a second study, a search technique is proposed for finding Bayesian network structures, capable of modeling complicated multivariate interactions, like the one exemplified by the parity function.
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Iclănzan, D., Dumitrescu, D., Hirsbrunner, B. (2010). Pairwise Interactions Induced Probabilistic Model Building. In: Chen, Yp. (eds) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12834-9_5
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DOI: https://doi.org/10.1007/978-3-642-12834-9_5
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