This paper presents an approach for building a multi-classifier system in Mean Field Genetic Algorithm (MGA) based inductive learning environments. Several base classifiers are combined with a meta-classifier that learns the bias of base classifiers so that it can draw a decision by combining predictions made by base classifiers. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). The proposed MGA combines the benefit of rapid convergence property of MFA and the effective genetic operations of SGA. © 2012 Springer-Verlag.
CITATION STYLE
Kim, Y., & Hong, C. (2012). A multi-classifier system using mean field genetic algorithm. In Communications in Computer and Information Science (Vol. 310 CCIS, pp. 121–128). https://doi.org/10.1007/978-3-642-32692-9_16
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