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Integrating user preferences into evolutionary multi-objective optimization


Jürgen Branke, Kalyan Deb



Veröffentlicht: Oktober 2004
Herausgeber: Yaochu Jin
Buchtitel: Knowledge Incorporation in Evolutionary Computation
Seiten: 461-478
Verlag: Springer

BibTeX

Kurzfassung
Many real-world optimization problems involve multiple, typically conflicting objectives. Often, it is very difficult to weigh the different criteria exactly before alternatives are known. Evolutionary multi-objective optimization usually solves this predicament by searching for the whole Pareto-optimal front of solutions. However, often the user has at least a vague idea about what kind of solutions might be preferred. In this chapter, we argue that such knowledge should be used to focus the search on the most interesting (from a user's perspective) areas of the Pareto-optimal front. To this end, we present and compare two methods which allow to integrate vague user preferences into evolutionary multi-objective algorithms. As we show, such methods may speed up the search and yield a more fine-grained selection of alternatives in the most relevant parts of the Pareto-optimal front.

ISBN: 3540229027
VG Wort-Seiten: 37



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet
Evolutionäre Algorithmen, Multikriterielle Optimierung