Interleaving Guidance in Evolutionary Multi-Objective Optimization


In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction… (More)
DOI: 10.1007/s11390-008-9114-2


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