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A New Step Size Update Strategy for CMA-ES in Multi-objective Optimisation

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Zheng Tan
Bo Yuan
Hao Wang
Miqing Li
Published in
Artificial Evolution
Publisher
Springer Nature Switzerland

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Abstract

This chapter introduces a novel step size update strategy for the multi-objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES). The strategy considers the extent of improvement or degradation in non-dominated fronts, making it more adaptive and effective. The text begins with an overview of MO-CMA-ES and its applications, followed by a detailed explanation of the new step size update strategy. The strategy is evaluated through extensive experiments on the bbob-biobj test suite, demonstrating its superiority in accelerating convergence speed. The chapter also discusses the impact of problem dimensionality and search budget on the performance of the proposed strategy. The results show that the new strategy outperforms existing methods, particularly in high-dimensional problems. The conclusion highlights the potential of the strategy to improve the search ability of MO-CMA-ES and suggests future research directions.
AI Generated

This summary of the content was generated with the help of AI.

This work was supported by National Natural Science Foundation of China (Grant No. 62476119) and the University Key Laboratory of Evolving Intelligent Systems of Guangdong Province (Grant No. 2017KSYS008).

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previous chapter Symbolic Regression of Confidence Intervals for Conformal Prediction
next chapter UMDA with Random Affine Maps
Title
A New Step Size Update Strategy for CMA-ES in Multi-objective Optimisation
Authors
Zheng Tan
Bo Yuan
Hao Wang
Miqing Li
Copyright Year
2026
Book
Artificial Evolution

Print ISBN: 978-3-032-07997-8

Electronic ISBN: 978-3-032-07998-5

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07998-5_15
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