Abstract
In optimization tasks that deal with real-world applications noise is very common leading to degradation of the performance of Evolution Strategies. We will consider the quality gain of an (1,λ)-ES under noisy fitness evaluations for arbitrary fitness functions. The equation developed will be applied to several test functions to check its predictive quality.
This work was supported by the Deutsche Forschungsgemeinschaft (DFG) as part of the Collaborative Research Center (SFB) 531.
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Beyer, HG., Meyer-Nieberg, S. (2004). On the Quality Gain of (1,λ)-ES Under Fitness Noise. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_1
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DOI: https://doi.org/10.1007/978-3-540-30217-9_1
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