AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Approximation-Guided Evolutionary Multi-Objective Optimization
Karl Bringmann, Tobias Friedrich, Frank Neumann, Markus Wagner

Last modified: 2011-06-28


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.

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