Abstract
An efficient evolutionary algorithm (EA) for the multiobjective optimization problems is proposed. To reduce the computational cost, a variant of k-d tree is used in our approach to preserve all nondominated solutions. Our experiments demonstrate that the algorithm outperforms the other popular multiobjective EA's, especially for the higher dimensional cases.
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