Abstract
We use methods from Markov chain theory to analyze the performance of some simple GA models on a class of deceptive objective functions. We consider the invariant distribution, the average expected hitting time, the mixing rate, and limits of these quantities as the objective function becomes more and more highly skewed.
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Acknowledgements
The work of FM is partially supported by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (238549-2012). The paper contains some results from the MSc thesis of the first author, and her work is supported by the Saudi Cultural Bureau.
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Alwadani, S., Mendivil, F., Shonkwiler, R. (2017). Limiting Distribution and Mixing Time for Genetic Algorithms. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_5
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DOI: https://doi.org/10.1007/978-3-319-50920-4_5
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