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Network representation of the game of life

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Języki publikacji
EN
Abstrakty
EN
The Game of Life (Life) is one of the most famous cellular automata. The main purpose of this article is to present a network representation of Life as an application of the approach proposed in our previous papers. This network representation has made it possible to investigate Life using a network theory. Some well-known Life patterns are illustrated by using the corresponding clustered networks. The visualization of Life’s rest state reveals the underlying tension as a complex network. The typical network parameters show the characteristics of Life as a Wolfram’s class IV rule. In particular, the in-degree distribution of the derived network from a Life’s rest state shows a scale-free nature, which could be related to the evidence of self-organized criticality.
Rocznik
Strony
233--240
Opis fizyczny
Bibliogr. 35 poz., rys.
Twórcy
autor
  • Department of Media and Information, BAIKA Women’s University, 2-19-5, Shukuno-sho, Ibaraki 567-8578, Osaka, Japan
autor
  • Department of Media and Information, BAIKA Women’s University, 2-19-5, Shukuno-sho, Ibaraki 567-8578, Osaka, Japan
Bibliografia
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  • [9] L. D. T. Golomb, B. A. and T. J. Sejnowski. Sexnet: A neural network identifies sex from human faces. In Advances in Neural Information Processing Systems, R.P. Lippman, J. Moody, and D.S. Touretzky, eds., volume 3, page 572577, 1991.
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  • [19] P. Merz and B. Freisleben. A comparison of memetic algorithms, tabu search, and ant colonies for the quadratic assignment problem. In Proc. Congress on Evolutionary Computation, IEEE, pages 2063–2070. Press, 1999.
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  • [28] Y.Wang, K. Ricanek, C. Chen, and Y. Chang. Gender classification from infants to seniors. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on, pages 1 –6, 2010.
  • [29] Z.-H. Wang and Z.-C. Mu. Gender classification using selected independent-features based on genetic algorithm. In Machine Learning and Cybernetics, 2009 International Conference on, volume 1, pages 394 –398, july 2009.
  • [30] Z. Yang and H. Ai. Demographic classification with local binary patterns. In S.-W. Lee and S. Li, editors, Advances in Biometrics, volume 4642 of Lecture Notes in Computer Science, pages 464–473. Springer Berlin / Heidelberg, 2007.
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  • [32] J.-H. Yoo, D. Hwang, and M. S. Nixon. Gender classification in human gait using support vector machine. In ACIVS, pages 138–145, 2005.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-08a324c4-fd99-4aa1-8169-2a29e7b7a9ad
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