Actively searching for committees of RBF networks using Bayesian evolutionary computation

Committee machines are known to improve generalization performance by combining the predictions of many different individual learners. Evolutionary algorithms generate multiple models that can be combined to build a committee machine. This paper uses Bayesian evolutionary algorithms (BEAs) as a solution to evolve individual learners and build a committee machine. BEAs are based on the Bayesian evolutionary framework in which evolutionary computation is the process of repeatedly updating the posterior distribution of a population to find an individual with the maximum posteriori probability. BEAs evolve the number of centroids and the centroids´ positions and widths for radial basis function (RBF) networks which are individual learners, and then the algorithms find an optimal committee from many different individuals. Empirical results show the machine´s convergence characteristics and accuracy