Do not match, inherit

Xavier Llorà, Kumara Sastry, Tian-Li Yu, David E. Goldberg
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
A byproduct benefit of using probabilistic model-building genetic algorithms is the creation of cheap and accurate surrogate models. Learning classifier systems-and geneticsbased machine learning in general-can greatly benefit from such surrogates which may replace the costly matching procedure of a rule against large data sets. In this paper we investigate the accuracy of such surrogate fitness functions when coupled with the probabilistic models evolved by the χ-ary extended compact
more » ... d compact classifier system (χeCCS). To achieve such a goal, we show the need that the probabilistic models should be able to represent all the accurate basis functions required for creating an accurate surrogate. We also introduce a procedure to transform populations of rules based into dependency structure matrices (DSMs) which allows building accurate models of overlapping building blocks-a necessary condition to accurately estimate the fitness of the evolved rules.
doi:10.1145/1276958.1277319 dblp:conf/gecco/LloraSYG07 fatcat:kkp3t5jbzvbx7hpjqtnpcjaqa4