Wave Propagation in Filamental Cellular Automata

Motivated by questions in biology and distributed computing, we investigate the behaviour of particular cellular automata, modelled as one-dimensional arrays of identical finite automata. We investigate what sort of self-stabilising cooperative behaviour these can induce in terms of waves of cellular state changes along a filament of cells. We discover what the minimum requirements are, in terms of numbers of states and the range of communication between automata, to observe this for individual filaments. We also discover that populations of growing filaments may have useful features that the individual filament does not have, and we give the results of numerical simulations.

[1]  Bastien Chopard,et al.  Cellular Automata Modeling of Physical Systems: Index , 1998 .

[2]  Edsger W. Dijkstra A belated proof of self-stabilization , 2005, Distributed Computing.

[3]  Bastien Chopard,et al.  Cellular Automata Modeling of Physical Systems , 1999, Encyclopedia of Complexity and Systems Science.

[4]  Jean-Philippe Rennard,et al.  Handbook of Research on Nature-inspired Computing for Economics and Management , 2006 .

[5]  Hongwei Mo,et al.  Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies , 2008 .

[6]  Jan K. Pachl,et al.  Uniform self-stabilizing rings , 1988, TOPL.

[7]  Álvaro Gomes,et al.  Incorporation of Preferences in an Evolutionary Algorithm Using an Outranking Relation: The EvABOR Approach , 2011, Int. J. Nat. Comput. Res..

[8]  James P. Crutchfield,et al.  Evolving One Dimensional Cellular Automata to Perform Non-trivial Collective Behavior Task: One Case Study , 2002, International Conference on Computational Science.

[9]  E. F. Moore Sequential Machines: Selected Papers , 1964 .

[10]  Lenka Lhotska,et al.  Optimizing Society: The Social Impact Theory Based Optimizer , 2009 .

[11]  G B Ermentrout,et al.  Cellular automata approaches to biological modeling. , 1993, Journal of theoretical biology.

[12]  F. H. Wong,et al.  Existence of Positive Solutions of Nonlinear Second-Order M-Point Boundary Value Problem , 2011, Int. J. Artif. Life Res..

[13]  Eleonora Bilotta,et al.  Cellular Automata and Complex Systems: Methods for Modeling Biological Phenomena , 2010 .

[14]  R. Chakrabarti,et al.  A Dynamic Agent-Based Model of Corruption , 2007 .

[15]  Andreas Deutsch,et al.  Cellular Automaton Modeling of Biological Pattern Formation - Characterization, Applications, and Analysis , 2005, Modeling and simulation in science, engineering and technology.

[16]  Edsger W. Dijkstra,et al.  Self-stabilizing systems in spite of distributed control , 1974, CACM.

[17]  H. Meinhardt,et al.  Biological pattern formation: fmm basic mechanisms ta complex structures , 1994 .

[18]  James P. Crutchfield,et al.  Evolving Globally Synchronized Cellular Automata , 1995, ICGA.

[19]  Jacques Mazoyer,et al.  An Overview of the Firing Squad Synchronization Problem , 1986, Automata Networks.

[20]  S. Hastings,et al.  Spatial Patterns for Discrete Models of Diffusion in Excitable Media , 1978 .

[21]  Alexandre C. B. Delbem,et al.  Decomposition of Black-Box Optimization Problems by Community Detection in Bayesian Networks , 2012, Int. J. Nat. Comput. Res..

[22]  Janito Vaqueiro Ferreira,et al.  Analysis of a Step-Based Watershed Algorithm Using CUDA , 2010, Int. J. Nat. Comput. Res..

[23]  Eleonora Bilotta,et al.  The Discovery of Complex Rules , 2010 .

[24]  Kamarulzaman Ab. Aziz,et al.  Coverage Maximization and Energy Conservation for Mobile Wireless Sensor Networks: A Two Phase Particle Swarm Optimization Algorithm , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.

[25]  Yi Jiang,et al.  On Cellular Automaton Approaches to Modeling Biological Cells , 2003, Mathematical Systems Theory in Biology, Communications, Computation, and Finance.

[26]  Cristina P. Santos,et al.  Head Motion Stabilization During Quadruped Robot Locomotion: Combining CPGs and Stochastic Optimization Methods , 2011, Int. J. Nat. Comput. Res..