Abstract
We report on the results of the first known use of Genetic Algorithms (GAs) to find highly nonlinear Boolean functions. The basic method, using a new breeding procedure, is shown to be several orders of magnitude faster than random search in locating Boolean functions with very high nonlinearity. When a directed hill climbing method is employed, the results are even better. The performance of random searches is used as a bench mark to assess the effectiveness of a basic GA, a directed hill climbing method, and a GA with hill climbing. The selection of GA parameters and convergence issues are discussed. Finally some future directions of this research are given.
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© 1997 Springer-Verlag
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Millan, W., Clark, A., Dawson, E. (1997). An effective genetic algorithm for finding highly nonlinear boolean functions. In: Han, Y., Okamoto, T., Qing, S. (eds) Information and Communications Security. ICICS 1997. Lecture Notes in Computer Science, vol 1334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028471
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DOI: https://doi.org/10.1007/BFb0028471
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