Abstract
We address the problem of expensive multi-objective optimization using a machine learning assisted model of evolutionary computation. Specifically, we formulate a meta-objective function tailored to the framework of MOEA/D, which can be solved by means of supervised regression learning using the Support Vector Machine (SVM) algorithm. The learned model constitutes the knowledge which can be then utilized to guide the evolution process within MOEA/D so as to reach better regions in the search space more quickly. Simulation results on a variety of benchmark problems show that the machine-learning enhanced MOEA/D is able to obtain better estimation of Pareto fronts when the allowed computational budget, measured in terms of number of objective function evaluation, is scarce.
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Liau, Y.S., Tan, K.C., Hu, J., Qiu, X., Gee, S.B. (2013). Machine Learning Enhanced Multi-Objective Evolutionary Algorithm Based on Decomposition. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_67
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DOI: https://doi.org/10.1007/978-3-642-41278-3_67
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