A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets
Zusammenfassung
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 biobjective 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 effcient means to accurately approximate the desired aspiration set.
Schlüsselwörter
multi-objective optimization; aspiration set; preferences
Zitieren als
Rudolph, G., Schütze, O., Grimme, C., & Trautmann, H. (2014). A Multiobjective Evolutionary Algorithm Guided by Averaged Hausdorff Distance to Aspiration Sets. In Tantar, A., Tantar, E., Sun, J., Zhang, W., Ding, Q., Schütze, O., Emmerich, M., Legrand, P., Del, M. P., & Coello, C. C. (Eds.), EVOLVE — A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (pp. 261–273). Advances in Intelligent Systems and Computing: Vol. 288. Springer International Publishing.Details
Publikationstyp
Forschungsartikel (Buchbeitrag)
Begutachtet
Ja
Publikationsstatus
Veröffentlicht
Jahr
2014
Buchtitel
EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V
Herausgeber
Tantar A, Tantar E, Sun J, Zhang W, Ding Q, Schütze O, Emmerich M, Legrand P, Del Moral P, Coello Coello CA
Erste Seite
261
Letzte Seite
273
Band
288
Reihe
Advances in Intelligent Systems and Computing
Verlag
Springer International Publishing
Sprache
Englisch
ISBN
978-3-319-07493-1
DOI
Gesamter Text