Additive approximations of Pareto-optimal sets by evolutionary multi-objective algorithms
Date
2009
Authors
Horoba, C.
Neumann, F.
Editors
Garibay, I.I.
Jansen, T.
Wiegand, R.P.
Wu, A.S.
Jansen, T.
Wiegand, R.P.
Wu, A.S.
Advisors
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Conference paper
Citation
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms (FOGA' 09), 2009: pp.79-86
Statement of Responsibility
Christian Horoba and Frank Neumann
Conference Name
ACM SIGEVO Workshop on Foundations of Genetic Algorithms (10th : 2009 : Orlando, Florida)
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.
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Copyright 2009 ACM