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Type: Conference paper
Title: A modified indicator-based evolutionary algorithm (mIBEA)
Author: Li, W.
Ozcan, E.
John, R.
Drake, J.
Neumann, A.
Wagner, M.
Citation: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2017), 2017 / pp.1047-1054
Publisher: IEEE
Issue Date: 2017
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781509046027
ISSN: 0965-089X
Conference Name: IEEE Congress on Evolutionary Computation (CEC 2017) (05 Jun 2017 - 08 Jun 2017 : San Sebastián, SPAIN)
Statement of
Wenwen Li, Ender Özcan, Robert John, John H. Drake, Aneta Neumann, Markus Wagner
Abstract: Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations.
Rights: ©2017 IEEE
RMID: 0030072972
DOI: 10.1109/CEC.2017.7969423
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Appears in Collections:Computer Science publications

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