An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization

Daniel HERRING; Michael KIRLEY; Xin YAO

doi:10.1145/3583133.3590624

An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization

Daniel HERRING, Michael KIRLEY, Xin YAO

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

1 Citation (Scopus)

Abstract

Dynamic Multi-objective Optimization problems (DMOPs) can represent formulations of complex realistic scenarios in industrial, logistics and energy domains. Consistent and comparable testing on DMOP benchmarks has seen recent progress in tools for comprehensive and reproducible testing. Ranges of possible dynamic instances of DMOPs can be generated defined by their frequency and severity of change parameters. Here, a combination of machine learning and evolutionary algorithm techniques allow for an efficient determination of algorithm performance limits. An iterative Support Vector Machine model is integrated with the existing Dynamic Parameter Testing Platform (DPTP) for selective (rather than exhaustive) evaluation of dynamic instances to inform on algorithm capability. © 2023 Copyright held by the owner/author(s).

Original language	English
Title of host publication	GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
Editors	Sara SILVA, Luís PAQUETE
Place of Publication	New York
Publisher	Association for Computing Machinery, Inc
Pages	367-370
Number of pages	4
ISBN (Electronic)	9798400701207
ISBN (Print)	9798400701207
DOIs	https://doi.org/10.1145/3583133.3590624
Publication status	Published - 15 Jul 2023
Externally published	Yes
Event	Genetic and Evolutionary Computation Conference 2023 - Lisbon, Portugal Duration: 15 Jul 2023 → 19 Jul 2023

Conference

Conference	Genetic and Evolutionary Computation Conference 2023
Abbreviated title	GECCO’23 Companion
Country/Territory	Portugal
City	Lisbon
Period	15/07/23 → 19/07/23

Bibliographical note

Publisher Copyright:
© 2023 Copyright held by the owner/author(s).

Funding

This research was partially funded by the Australian Government through the Australian Research Council Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Project ID IC200100009. HPC facilities BlueBEAR (University of Birmingham) and Spartan (University of Melbourne) facilitated this research.

Keywords

dynamic multi-objective optimization
dynamic optimization
evolutionary algorithm
machine learning
support vector machine

Access to Document

10.1145/3583133.3590624

Cite this

HERRING, D., KIRLEY, M., & YAO, X. (2023). An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization. In S. SILVA, & L. PAQUETE (Eds.), GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion (pp. 367-370). Association for Computing Machinery, Inc. https://doi.org/10.1145/3583133.3590624

HERRING, Daniel ; KIRLEY, Michael ; YAO, Xin. / An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization. GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. editor / Sara SILVA ; Luís PAQUETE. New York : Association for Computing Machinery, Inc, 2023. pp. 367-370

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HERRING, D, KIRLEY, M & YAO, X 2023, An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization. in S SILVA & L PAQUETE (eds), GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, New York, pp. 367-370, Genetic and Evolutionary Computation Conference 2023, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590624

An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization. / HERRING, Daniel; KIRLEY, Michael; YAO, Xin.
GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. ed. / Sara SILVA; Luís PAQUETE. New York: Association for Computing Machinery, Inc, 2023. p. 367-370.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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HERRING D, KIRLEY M, YAO X. An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization. In SILVA S, PAQUETE L, editors, GECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. New York: Association for Computing Machinery, Inc. 2023. p. 367-370 doi: 10.1145/3583133.3590624

An Iterative Machine Learning Approach to Informative Performance Reporting in Dynamic Multi-Objective Optimization

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