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Approximation-guided Evolutionary Multi-objective Optimization

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44182

Bringmann,  Karl
Algorithms and Complexity, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44447

Friedrich,  Tobias
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45115

Neumann,  Frank
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45683

Wagner,  Markus
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Zitation

Bringmann, K., Friedrich, T., Neumann, F., & Wagner, M. (2011). Approximation-guided Evolutionary Multi-objective Optimization. In T. Walsh (Ed.), Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (pp. 1198-1203). Menlo Park, CA: AAAI Press. doi:10.5591/978-1-57735-516-8/IJCAI11-204.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-11EE-C
Zusammenfassung
Multi-objective optimization problems arise frequently in applications but can often only be solved approximately by heuristic approaches. Evolutionary algorithms have been widely used to tackle multi-objective problems. These algorithms use different measures to ensure diversity in the objective space but are not guided by a formal notion of approximation. We present a new framework of an evolutionary algorithm for multi-objective optimization that allows to work with a formal notion of approximation. Our experimental results show that our approach outperforms state-of-the-art evolutionary algorithms in terms of the quality of the approximation that is obtained in particular for problems with many objectives.