Explaining Memristive Reservoir Computing Through Evolving Feature Attribution

Xinming SHI, Zilu WANG, Leandro L. MINKU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


Memristive Reservoir Computing (MRC) is a promising computing architecture for time series tasks, but lacks explainability, leading to unreliable predictions. To address this issue, we propose an evolutionary framework to explain the time series predictions of MRC systems. Our proposed approach attributes the feature importance of the time series via an evolutionary approach to explain the predictions. Our experiments show that our approach successfully identified the most influential factors, demonstrating the effectiveness of our design and its superiority in terms of explanation compared to state-of-the-art methods. © 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 work was supported by NSFC (Grant No. 62250710682), the Research Institute of Trustworthy Autonomous Systems (RITAS), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531).


  • evolutionary algorithm
  • Explainability
  • memristor
  • reservoir computing


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