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New Solutions for the Density Classification Task in One Dimensional Cellular Automata

  • Zakaria LaboudiEmail author
  • Salim Chikhi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)

Abstract

The density classification task is one of the most studied benchmark problems to analyze emergent collective computations resulting from local interactions within cellular automata. Solutions for this task were produced by means of different training methods, in particular the automatic design through evolutionary algorithms. This is tied to the fact that there is still a lack of thorough understanding of computations’ nature within cellular automata, which impedes writing efficient local rules. In this paper, we propose a new procedure for solving the density classification task using handwritten local rules in the case of one dimensional cellular automata of radius r = 4. The experimental results show that the newly designed rules outperform the currently best known solutions. This is important since it helps, on the one hand, to deepen our knowledge about selecting appropriate local rules to solve computational tasks and, to improve our general understanding of computations carried out by cellular automata, on the other hand.

Keywords

Emergent computation Cellular automata Density classification task Symmetry property Number-conserving property 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.RELA(CS)2 LaboratoryUniversity of Oum El-BouaghiOum El-BouaghiAlgeria
  2. 2.MISC LaboratoryUniversity of Constantine 2ConstantineAlgeria

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