Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/127218
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Type: Conference paper
Title: Evolutionary diversity optimization using multi-objective indicators
Author: Neumann, A.
Gao, W.
Wagner, M.
Neumann, F.
Citation: GECCO '19: Proceedings of the 2109 Genetic and Evolutionary Computation Conference, 2019 / vol.abs/1811.06804, pp.1-9
Publisher: Association for Computing Machinery
Publisher Place: online
Issue Date: 2019
ISBN: 9781450361118
Conference Name: Genetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)
Statement of
Responsibility: 
Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann
Abstract: Evolutionary diversity optimization aims to compute a set of solutions that are diverse in the search space or instance feature space, and where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.
Rights: © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.
RMID: 0030134805
DOI: 10.1145/3321707.3321796
Grant ID: http://purl.org/au-research/grants/arc/DE160100850
http://purl.org/au-research/grants/arc/DP160102401
http://purl.org/au-research/grants/arc/DP190103894
Appears in Collections:Computer Science publications

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