Understanding Performance

Part of the Natural Computing Series book series (NCS)


Progress Rate Subset Selection Threshold Selection Correct Selection Threshold Acceptance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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8.6 Further Reading

  1. Arnold, D. V. & Beyer, H.-G. (2003). A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications, 24(1), 135–159.CrossRefMathSciNetGoogle Scholar
  2. Bechhofer, R. E., Santner, T. J., & Goldsman, D. M. (1995). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. New York NY: Wiley.Google Scholar
  3. Beyer, H.-G. (2001). The Theory of Evolution Strategies. Berlin, Heidelberg, New York: Springer.Google Scholar
  4. Branke, J., Chick, S., & Schmidt, C. (2005). New developments in ranking and selection: An empirical comparison of the three main approaches. In M. E. Kuhl & others (Eds.), Proceedings of the 2005 Winter Simulation Conference (pp. 708–717). Piscataway NJ: IEEE.Google Scholar
  5. Gigerenzer, G. & Selten, R., Eds. (2002). Bounded Rationality: The Adaptive Toolbox. Cambridge MA: MIT Press.Google Scholar
  6. Gigerenzer, G., Todd, P. M., & the ABC research group (1999). Simple Heuristics That Make Us Smart. New York NY: Oxford University Press.Google Scholar
  7. Jin, Y. & Branke, J. (2005). Evolutionary optimization in uncertain environments—a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.CrossRefGoogle Scholar
  8. Rubinstein, A. (1998). Modeling Bounded Rationality. Cambridge MA: MIT Press.Google Scholar
  9. Rudolph, G. (1997a). Convergence Properties of Evolutionary Algorithms. Hamburg, Germany: Kovač.Google Scholar
  10. Schwefel, H.-P. (1995). Evolution and Optimum Seeking. Sixth-Generation Computer Technology. New York NY: Wiley.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2006

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