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An effective genetic algorithm for finding highly nonlinear boolean functions

  • Session 5: Boolean Functions and Stream Ciphers
  • Conference paper
  • First Online:
Information and Communications Security (ICICS 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1334))

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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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Author information

Authors and Affiliations

  1. Information Security Research Centre, Queensland University of Technology, GPO Box 2434, 4001, Brisbane, Australia

    William Millan, Andrew Clark & Ed Dawson

Authors
  1. William Millan
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  2. Andrew Clark
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  3. Ed Dawson
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Editor information

Yongfei Han Tatsuaki Okamoto Sihan Qing

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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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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63696-0

  • Online ISBN: 978-3-540-69628-5

  • eBook Packages: Springer Book Archive

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