Efficient relevance estimation and value calibration of evolutionary algorithm parameters

  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},
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

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