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Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/74049

Type: Conference paper
Title: An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives
Author: Yuen, Joseph Sze Zho
Gao, Sophia
Wagner, Markus
Neumann, Frank
Citation: Proceedings of the 2012 IEEE Congress on Evolutionary Computation, held in Brisbane, 10-15 June, 2012: pp.1-8
Publisher: IEEE
Issue Date: 2012
ISBN: 9781467315104
Cover art
9781467315081
Conference Name: IEEE Congress on Evolutionary Computation (2012 : Brisbane, Qld.)
CEC 2012
School/Discipline: School of Computer Science
Statement of
Responsibility: 
J. Yuen, S. Gao, M. Wagner and F. Neumann
Abstract: Archives have been widely used in evolutionary multi-objective optimization in order to store the optimal points found so far during the optimization process. Usually the size of an archive is bounded which means that the number of points it can store is limited. This implies that knowledge about the set of non-dominated solutions that has been obtained during the optimization process gets lost. Working with unbounded archives allows to keep this knowledge which can be useful for the progress of an evolutionary multi-objective algorithm. In this paper, we propose an adaptive data structure for dealing with unbounded archives. This data structure allows to traverse the archive efficiently and can also be used for sampling solutions from the archive which can be used for reproduction.
Rights: © Copyright 2012 IEEE - All rights reserved.
RMID: 0020122216
DOI: 10.1109/CEC.2012.6256468
Appears in Collections:Computer Science publications
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