Efficient relevance estimation and value calibration of evolutionary algorithm parameters

@article{Nannen2007EfficientRE,
  title={Efficient relevance estimation and value calibration of evolutionary algorithm parameters},
  author={Volker Nannen and A. E. Eiben},
  journal={2007 IEEE Congress on Evolutionary Computation},
  year={2007},
  pages={103-110}
}
Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. The standard statistical method to reduce variance is measurement replication, i.e., averaging over several test runs with identical parameter settings. The computational cost of measurement replication scales with the variance and is often too high to allow for results of statistical significance. In this paper we study… CONTINUE READING
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A Method for Parameter Calibration and Relevance Estimation in Evolutionary Algorithms

  • Martin Pelikan, David E. Goldberg
  • 2006

editors

  • Volker Nannen, A. E. Eiben. A Method for Parameter Calibration, Relevance Estimation in Evolutionary Algorithms. In Maarten Keijzer
  • Genetic and Evolutionary Computation Conference…
  • 2006
1 Excerpt

The Estimation of Distributions and the Minimum Relative Entropy Principle

  • R. Höns
  • Evolutionary Computation
  • 1997

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