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A Robust Statistical Framework for the Analysis of the Performances of Stochastic Optimization Algorithms Using the Principles of Severity

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Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

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Abstract

Meta-heuristic stochastic optimization algorithms are predominantly used to solve complex real-world problems. Numerous new nature-inspired meta-heuristics are being proposed to address various open challenges. Since many heuristics are stochastic, they could yield different solutions to the same problem for different runs. Hence, there is a need for stringent in-depth statistical analysis of the performances of stochastic optimization algorithms. The proposed severity framework enables researchers and practitioners to define application-specific and meaningful performance evaluation metric that evaluates the magnitude of the performance improvement achieved, which is not only of statistical significance but also of practical relevance.

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Notes

  1. 1.

    Note that in the proposed framework, we do not assume normality of the data. Here it is assumed to simplify the explanation of the concept and without the loss of generality, the concept can be adapted to the cases where the distribution is not known.

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Author information

Authors and Affiliations

  1. Institute for Data Science, Engineering, and Analytics, TH Köln, Steinmüllerallee 1, 51643, Gummersbach, Germany

    Sowmya Chandrasekaran & Thomas Bartz-Beielstein

Authors
  1. Sowmya Chandrasekaran
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  2. Thomas Bartz-Beielstein
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Corresponding author

Correspondence to Sowmya Chandrasekaran .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Chandrasekaran, S., Bartz-Beielstein, T. (2023). A Robust Statistical Framework for the Analysis of the Performances of Stochastic Optimization Algorithms Using the Principles of Severity. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_28

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-30229-9

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