The Journal of Supercomputing

, Volume 63, Issue 3, pp 800–815 | Cite as

Learning cellular automata rules for binary classification problem

  • Anna Piwonska
  • Franciszek Seredynski
  • Miroslaw Szaban


This paper proposes a cellular automata-based solution of a binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an excellent performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.


Cellular automata Binary classification problem Genetic algorithm 



This research was supported by the grant S/WI/2/2008 from Bialystok University of Technology.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Anna Piwonska
    • 1
  • Franciszek Seredynski
    • 2
  • Miroslaw Szaban
    • 3
  1. 1.Computer Science FacultyBialystok University of TechnologyBialystokPoland
  2. 2.Poland and Polish-Japanese Institute of Information TechnologyCardinal Stefan Wyszynski UniversityWarsawPoland
  3. 3.Institute of Computer ScienceUniversity of Natural Sciences and HumanitiesSiedlcePoland

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