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Adaptive Parent Population Sizing in Evolution Strategies.

G Jake LaPorte1, Juergen Branke2, Chun-Hung Chen3

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Adaptive population sizing enhances evolution strategies by dynamically adjusting parent population size for maximum fitness gain. This study introduces novel adaptive methods outperforming fixed-size strategies, even CMA-ES.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Evolution strategies are optimization algorithms inspired by natural selection.
  • Adaptive population sizing aims to dynamically adjust population size for improved performance.
  • Previous methods often used fixed population sizes, limiting adaptability.

Purpose of the Study:

  • To develop and evaluate adaptive population sizing methods for (μ/μ, λ) evolution strategies.
  • To enhance the progress and efficiency of evolutionary computation.
  • To compare adaptive methods against fixed population sizes and CMA-ES.

Main Methods:

  • Derived two adaptive population sizing approaches based on sphere model considerations.
  • Empirically tested approaches on the sphere model with normalized and cumulative mutation strength adaptation.
  • Compared the proposed methods against fixed population sizes and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) on general functions.

Main Results:

  • The proposed adaptive population sizing methods consistently yielded superior results compared to fixed population sizes.
  • Performance improvements were observed even when compared to the advanced CMA-ES.
  • The adaptive strategies demonstrated enhanced fitness gain across various evolutionary processes.

Conclusions:

  • Adaptive population sizing is a viable and effective technique for improving evolution strategies.
  • The developed methods offer a significant advancement over traditional fixed-size approaches.
  • This research contributes to the field of evolutionary computation by providing more efficient optimization tools.