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A modified indicator-based evolutionary algorithm (mIBEA)

Li, Wenwen; Özcan, Ender; John, Robert; Drake, John H.; Neumann, Aneta; Wagner, Markus

Authors

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.

Publication Date Jun 5, 2017
Peer Reviewed Peer Reviewed
APA6 Citation Li, W., Özcan, E., John, R., Drake, J. H., Neumann, A., & Wagner, M. (2017). A modified indicator-based evolutionary algorithm (mIBEA)
Keywords Sociology, Statistics, Evolutionary computation, Electronic mail, Optimization, Benchmark testing, Computer science
Publisher URL http://ieeexplore.ieee.org/abstract/document/7969423/
Related Public URLs http://cec2017.org/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2017 IEEE Congress on Evolutionary Computation (CEC). Piscataway, N.J. : IEEE, c2017. Electronic ISBN: 978-1-5090-4601-0. pp. 1047-1054, doi:10.1109/CEC.2017.7969423 © 2017 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.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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