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|Title:||Machine learning enhanced multi-objective evolutionary algorithm based on decomposition|
|Keywords:||Evolutionary multi-objective optimization|
support vector machine
|Source:||Liau, Y.S.,Tan, K.C.,Hu, J.,Qiu, X.,Gee, S.B. (2013). Machine learning enhanced multi-objective evolutionary algorithm based on decomposition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8206 LNCS : 553-560. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-41278-3_67|
|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. © 2013 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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