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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:EI、CPCI-S、Scopus

Page Number:521-524

Key Words:ensemble of surrogates; evolutionary multi-agent system; cross-validation

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.