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Multi-Space Evolutionary Search for Large-Scale Multi-Objective Optimization

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Evolutionary Multi-Task Optimization

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Abstract

Besides solving large-scale single objective optimization problems, this chapter further demonstrate the multi-space evolutionary search for large-scale multi-objective optimization by using the evolutionary multitasking paradigm of MFO, termed MOEMT. The presented MOEMT first constructs several simplified problem spaces in a multi-variation manner to assist target optimization.

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Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Liang Feng

  2. Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Resea, Singapore, Singapore

    Abhishek Gupta

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Kay Tan

  4. School of Computer Science & Engineering, Nanyang Technological University, Singapore, Singapore

    Yew Ong

Authors
  1. Liang Feng
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  2. Abhishek Gupta
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  3. Kay Tan
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  4. Yew Ong
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Feng, L., Gupta, A., Tan, K., Ong, Y. (2023). Multi-Space Evolutionary Search for Large-Scale Multi-Objective Optimization. In: Evolutionary Multi-Task Optimization. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-19-5650-8_9

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  • DOI: https://doi.org/10.1007/978-981-19-5650-8_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5649-2

  • Online ISBN: 978-981-19-5650-8

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