Finding State Transition Functions of One-Dimensional Cellular Automata by Evolutionary Algorithms


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 5, pp. 187-192, May. 2019
https://doi.org/10.3745/KTSDE.2019.8.5.187, Full Text:
Keywords: cellular automata, Majority Problem, Synchronization Problem, Evolutionary Algorithms, Conditionally Matching Rules
Abstract

Majority problem and synchronization problem on cellular automata(CA) are hard to solve, since they are global problems while CA operate on local information. This paper proposes a way to find state transition rules of these problems. The rules of CA are represented as CMR(conditionally matching rules) and evolutionary algorithms are applied to find rules. We find many solution rules to these problems, compared the results with the previous studies, and demonstrated the effectiveness of CMR on one-dimensional cellular automata.


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Cite this article
[IEEE Style]
J. Park, S. Wang and K. Wee, "Finding State Transition Functions of One-Dimensional Cellular Automata by Evolutionary Algorithms," KIPS Transactions on Software and Data Engineering, vol. 8, no. 5, pp. 187-192, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.5.187.

[ACM Style]
Jongwoo Park, Sehee Wang, and Kyubum Wee. 2019. Finding State Transition Functions of One-Dimensional Cellular Automata by Evolutionary Algorithms. KIPS Transactions on Software and Data Engineering, 8, 5, (2019), 187-192. DOI: https://doi.org/10.3745/KTSDE.2019.8.5.187.