Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113670
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Type: Conference paper
Title: Discrepancy-based evolutionary diversity optimization
Author: Neumann, A.
Gao, W.
Doerr, C.
Neumann, F.
Wagner, M.
Citation: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018), 2018 / Aguirre, H.E., Takadama, K. (ed./s), pp.991-998
Publisher: Association for Computing Machinery
Issue Date: 2018
ISBN: 9781450356183
Conference Name: The Genetic and Evolutionary Computation Conference (GECCO 2018) (15 Jul 2018 - 19 Jul 2018 : Kyoto, JAPAN)
Statement of
Responsibility: 
Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner
Abstract: Diversity plays a crucial role in evolutionary computation. While diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence, the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years. Diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the Traveling Salesperson Problem. We consider discrepancy-based diversity optimization approaches for evolving diverse sets of images as well as instances of the Traveling Salesperson problem where a local search is not able to nd near optimal solutions. Our experimental investigations comparing three diversity optimization approaches show that a discrepancy-based diversity optimization approach using a tiebreaking rule based on weighted di erences to surrounding feature points provides the best results in terms of the star discrepancy measure.
Keywords: Diversity; evolutionary algorithms; features
Rights: © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.
RMID: 0030094314
DOI: 10.1145/3205455.3205532
Grant ID: http://purl.org/au-research/grants/arc/DP140103400
http://purl.org/au-research/grants/arc/DP160102401
Published version: http://doi.acm.org/10.1145/3205455
Appears in Collections:Computer Science publications

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