Abstract
The incorporation of expert knowledge into multiobjective optimization is an important issue which in this paper is reflected in terms of an aspiration set consisting of multiple reference points. The behaviour of the recently introduced evolutionary multiobjective algorithm AS-EMOA is analysed in detail and comparatively studied for bi-objective optimization problems w.r.t. R-NSGA2 and a respective variant. It will be shown that the averaged Hausdorff distance, integrated into AS-EMOA, is an efficient means to accurately approximate the desired aspiration set.
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Rudolph, G., Schütze, O., Grimme, C., Trautmann, H. (2014). A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In: Tantar, AA., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. Advances in Intelligent Systems and Computing, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-319-07494-8_18
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DOI: https://doi.org/10.1007/978-3-319-07494-8_18
Publisher Name: Springer, Cham
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