Abstract
This paper introduces a novel application of genetic algorithms for evolving optimal addition-subtraction sequences that allow one to perform pre- computations necessary in the window-based modular exponentiation methods. When the window size is large, the pre-processing step becomes very expensive. Evolved addition/addition-subtraction sequences are of minimal size so they allow one to perform exponentiation with a minimal number of multiplication and/or divisions and hence implementing efficiently the exponentiation operation.
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Nedjah, N., de Macedo Mourelle, L. (2003). Minimal Addition-Subtraction Sequences for Efficient Pre-processing in Large Window-Based Modular Exponentiation Using Genetic Algorithms. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_43
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DOI: https://doi.org/10.1007/978-3-540-45080-1_43
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