Abstract
Noise is a common problem encountered in real-world optimization. Although it is folklore that evolution strategies perform well in the presence of noise, even their performance is degraded. One effect on which we will focus in this paper is the reaching of a steady state that deviates from the actual optimal solution.
The quality gain is a local progress measure, describing the expected one-generation change of the fitness of the population. It can be used to derive evolution criteria and steady state conditions which can be utilized as a starting point to determine the final fitness error, i.e. the expected difference between the actual optimal fitness value and that of the steady state. We will demonstrate the approach by determining the final solution quality for two fitness functions.
This work was supported by the Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center SFB 531 at the University of Dortmund and by the Research Center for Process- and Product-Engineering at the Vorarlberg University of Applied Sciences.
We would like to thank especially the anonymous reviewer # 2 for pointing out some inconsistencies in the first version of this paper.
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Beyer, HG., Meyer-Nieberg, S. (2005). On the Prediction of the Solution Quality in Noisy Optimization. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds) Foundations of Genetic Algorithms. FOGA 2005. Lecture Notes in Computer Science, vol 3469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11513575_13
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DOI: https://doi.org/10.1007/11513575_13
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