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 ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publicationGECCO 2023 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
EditorsSara SILVA, Luís PAQUETE
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Print)9798400701207
Publication statusPublished - 15 Jul 2023
Externally publishedYes
EventGenetic and Evolutionary Computation Conference 2023 - Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023


ConferenceGenetic and Evolutionary Computation Conference 2023
Abbreviated titleGECCO’23 Companion

Bibliographical note

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.


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


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