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Type: Conference paper
Title: Additive approximations of Pareto-optimal sets by evolutionary multi-objective algorithms
Author: Horoba, C.
Neumann, F.
Citation: Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms (FOGA' 09), 2009: pp.79-86
Publisher: ACM Press
Publisher Place: New York
Issue Date: 2009
ISBN: 9781605584140
Conference Name: ACM SIGEVO Workshop on Foundations of Genetic Algorithms (10th : 2009 : Orlando, Florida)
Editor: Garibay, I.I.
Jansen, T.
Wiegand, R.P.
Wu, A.S.
Statement of
Christian Horoba and Frank Neumann
Abstract: Often the Pareto front of a multi-objective optimization problem grows exponentially with the problem size. In this case, it is not possible to compute the whole Pareto front efficiently and one is interested in good approximations. We consider how evolutionary algorithms can achieve such approximations by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the "-dominance approach and point out how and when such mechanisms provably help to obtain good additive approximations of the Pareto-optimal set.
Keywords: Algorithms
Rights: Copyright 2009 ACM
DOI: 10.1145/1527125.1527137
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Computer Science publications

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