Using gradient information for multi-objective problems in the evolutionary context

The goal of this research is to study the incorporation of gradient-based information when designing Multi-objective Evolutionary Algorithms (MOEAs). We analyze the benefits, and challenges, of using these well developed mathematical programming techniques in order to get hybrid MOEAs. Since we expect the new hybrid algorithms to search effectively and more efficiently than currently available MOEAs, a deeper study of the balance between the computational and the benefits of this coupling is highly necessary.

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