Ensemble of surrogates with an evolutionary multi-agent system

Abstract

We herein propose an evolutionary multi-agent system (EMAS for short) to build an ensemble of surrogates for prediction. In our EMAS, we employ six kinds of basic surrogates, including Gaussian process, Kriging model, polynomial response surface, radial basis function, radial basis function neural network, and support vector regression machine. We define each surrogate as one agent and co-evolve parameters of basic surrogates to obtain the evolutionary weighted average surrogate, where sample cross-validation errors evaluate an ensemble of surrogates. The preliminary results from predicting the benchmark function with high dimension showed the effectiveness of our EMAS for an ensemble of surrogates.