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Voronoi-based estimation of distribution algorithm for multi-objective optimization

Okabe, T, Jin, Y, Sendhoff, B and Olhofer, M (2004) Voronoi-based estimation of distribution algorithm for multi-objective optimization

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Abstract

The distribution of the Pareto-optimal solutions often has a clear structure. To adapt evolutionary algorithms to the structure of a multi-objective optimization problem, either an adaptive representation or adaptive genetic operators should be employed. In this paper, we suggest an estimation of distribution algorithm for solving multi-objective optimization, which is able to adjust its reproduction process to the problem structure. For this purpose, a new algorithm called Voronoi-based Estimation of Distribution Algorithm (VEDA) is proposed. In VEDA, a Voronoi diagram is used to construct stochastic models, based on which new offspring will be generated. Empirical comparisons of the VEDA with other estimation of distribution algorithms (EDAs) and the popular NSGA-II algorithm are carried out. In addition, representation of Pareto-optimal solutions using a mathematical model rather than a solution set is also discussed.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Okabe, TUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Sendhoff, BUNSPECIFIEDUNSPECIFIED
Olhofer, MUNSPECIFIEDUNSPECIFIED
Date : 2004
Identification Number : 10.1109/CEC.2004.1331086
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/PBLIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 31 Oct 2017 14:35
URI: http://epubs.surrey.ac.uk/id/eprint/532848

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