A Multi-classifier System Using Mean Field Genetic Algorithm

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.