Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/66830
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Type: Book chapter
Title: Approximating pareto-optimal sets using diversity strategies in evolutionary multi-objective optimization
Author: Horoba, C.
Neumann, F.
Citation: Advances in multi-objective nature inspired computing, 2010 / Coello Coello, C., Dhaenens, C., Jourdan, L. (ed./s), pp.23-44
Publisher: Springer
Publisher Place: Berlin
Issue Date: 2010
Series/Report no.: Studies in Computational Intelligence; vol. 272
ISBN: 9783642112171
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 an approximation by using different diversity mechanisms. We discuss some well-known approaches such as the density estimator and the ε -dominance approach and point out when and how such mechanisms provably help to obtain a good approximation of the Pareto-optimal set.
Rights: © Springer, Part of Springer Science+Business Media
RMID: 0020110542
DOI: 10.1007/978-3-642-11218-8_2
Published version: https://doi.org/10.1007/978-3-642-11218-8
Appears in Collections:Computer Science publications

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