Abstract
The significant effect of parameter settings on the success of the evolutionary optimization has led to a long history of research on parameter control, e.g., on mutation rates. However, few studies compare different tuning and control strategies under the same experimental condition. Objective of this paper is to give a comprehensive and fundamental comparison of tuning and control techniques of mutation rates employing the same algorithmic setting on a simple unimodal problem. After an analysis of various mutation rates for a (1+1)-EA on OneMax, we compare meta-evolution to Rechenberg’s 1/5th rule and self-adaptation.
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Kramer, O. (2013). On Mutation Rate Tuning and Control for the (1+1)-EA. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_9
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DOI: https://doi.org/10.1007/978-3-642-40942-4_9
Publisher Name: Springer, Berlin, Heidelberg
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