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8.6 Further Reading
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.
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.
Beyer, H.-G. (2001). The Theory of Evolution Strategies. Berlin, Heidelberg, New York: Springer.
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.
Gigerenzer, G. & Selten, R., Eds. (2002). Bounded Rationality: The Adaptive Toolbox. Cambridge MA: MIT Press.
Gigerenzer, G., Todd, P. M., & the ABC research group (1999). Simple Heuristics That Make Us Smart. New York NY: Oxford University Press.
Jin, Y. & Branke, J. (2005). Evolutionary optimization in uncertain environments—a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
Rubinstein, A. (1998). Modeling Bounded Rationality. Cambridge MA: MIT Press.
Rudolph, G. (1997a). Convergence Properties of Evolutionary Algorithms. Hamburg, Germany: Kovač.
Schwefel, H.-P. (1995). Evolution and Optimum Seeking. Sixth-Generation Computer Technology. New York NY: Wiley.
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(2006). Understanding Performance. In: Experimental Research in Evolutionary Computation. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32027-X_8
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