A Meta-Learning Approach based on Mean Field Genetic Algorithms

Mean Field Genetic Algorithm (MGA) is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). It combines benefit of rapid convergence property of MFA and effective genetic operations of SGA. This paper presents an approach for building a multi-classifier system in a MGA-based inductive learning environment. Multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The general classifier performs regular classification task. The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decisionmaking process or not. The paper discusses our approach in details and presents some empirical results that show the improvement we can achieve with our approach.