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Evolution-Based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes

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Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15151))

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Abstract

Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for complex feature interaction selection problems. MCES allows models within every population to evolve adaptively, selecting relevant feature interactions for different lake types and tasks. Evaluated on diverse lakes in the Midwestern USA, MCES not only consistently produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lake types, embodying the innovative concept of “AI from nature, for nature”.

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Acknowledgments

This research was partially supported by the University of Pittsburgh Center for Research Computing through the resources provided.

Author information

Authors and Affiliations

  1. University of Pittsburgh, Pittsburgh, USA

    Runlong Yu & Xiaowei Jia

  2. Aarhus University, Aarhus, Denmark

    Robert Ladwig

  3. University of Science and Technology of China, Hefei, China

    Xiang Xu

  4. Georgia Institute of Technology, Atlanta, USA

    Peijun Zhu

  5. University of Wisconsin-Madison, Madison, USA

    Paul C. Hanson

  6. University of Maryland, College Park, USA

    Yiqun Xie

Authors
  1. Runlong Yu
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  2. Robert Ladwig
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  3. Xiang Xu
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  4. Peijun Zhu
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  6. Yiqun Xie
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  7. Xiaowei Jia
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Corresponding author

Correspondence to Xiaowei Jia .

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Editors and Affiliations

  1. University of Applied Sciences Upper Austria, Wels, Austria

    Michael Affenzeller

  2. University of Applied Sciences Upper Austria, Hagenberg, Austria

    Stephan M. Winkler

  3. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  4. University of Paderborn, Paderborn, Germany

    Heike Trautmann

  5. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

  6. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  7. Leiden University, Leiden, The Netherlands

    Thomas Bäck

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Yu, R. et al. (2024). Evolution-Based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes. In: Affenzeller, M., et al. Parallel Problem Solving from Nature – PPSN XVIII. PPSN 2024. Lecture Notes in Computer Science, vol 15151. Springer, Cham. https://doi.org/10.1007/978-3-031-70085-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-70085-9_25

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