A memetic variant of R-NSGA-II for reference point problems

The task in multi-objective optimization is to optimize several objectives concurrently. Since the solution P of such problems is typically given by an entire set, the entire approximation of P is not always possible or even desired. Instead, it makes sense to concentrate on particular points or regions of the solution set. In case the decision maker has a certain idea about the performance of his/her product, reference point methods can be used to find the solutions that are closest to the given reference point. Evolutionary algorithms are advantageous for the treatment of such problems in particular if there are multiple reference points and/or the objectives are highly multi-modal, however, they suffer the general drawback of a slow convergence rate.

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