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Searching Cellular Automata Rules for Solving Two-Dimensional Binary Classification Problem

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7495)

Abstract

This paper proposes a cellular automata-based solution of a two-dimensional 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 very good 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.

Keywords

  • Cellular Automata
  • Binary Classification Problem
  • Genetic Algorithm

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Piwonska, A., Seredynski, F., Szaban, M. (2012). Searching Cellular Automata Rules for Solving Two-Dimensional Binary Classification Problem. In: Sirakoulis, G.C., Bandini, S. (eds) Cellular Automata. ACRI 2012. Lecture Notes in Computer Science, vol 7495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33350-7_13

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  • DOI: https://doi.org/10.1007/978-3-642-33350-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33349-1

  • Online ISBN: 978-3-642-33350-7

  • eBook Packages: Computer ScienceComputer Science (R0)