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Evolutionary multiobjective optimization (EMO) is an active research area in the field of evolutionary computation. EMO algorithms are designed to find a non-dominated solution set that approximates the entire Pareto front of a multiobjective optimization problem. Whereas EMO algorithms usually work well on two-objective and three-objective problems, their search ability is degraded by the increase...
It proposed an idea of using support vector machines (SVMs) to learn the efficient set of a multiple objective discrete optimization (MODO) problem. We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As the efficient set is learned at a single SVM implementation by using a group of seeds that symbolize efficient and dominated solutions. To be able...
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