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Speeding up many-objective optimization by Monte Carlo approximations
2013
Artificial Intelligence
Many state-of-the-art evolutionary vector optimization algorithms compute the contributing hypervolume for ranking candidate solutions. However, with an increasing number of objectives, calculating the volumes becomes intractable. Therefore, although hypervolume-based algorithms are often the method of choice for bi-criteria optimization, they are regarded as not suitable for manyobjective optimization. Recently, Monte Carlo methods have been derived and analyzed for approximating the
doi:10.1016/j.artint.2013.08.001
fatcat:klfbnun72jhmfdxzdezzmkubsq