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Version vom 15. August 2009, 17:32 Uhr
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
Evolutionäre Algorithmen, Multikriterielle Optimierung