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41. Fun With Cellular Automata
Playing with real time cellular automata is as much fun as putting grapes, marshmellow peeps, peppermint patties, bars of soap, and aluminum foil in the
http://www.donhopkins.com/home/catalog/art/cell.html
##### Fun with Cellular Automata, by Don Hopkins
Playing with real time Cellular Automata is as much fun as putting grapes, marshmellow peeps, peppermint patties, bars of soap, and aluminum foil in the microwave, but they're much easier to clean up, and they don't smell so bad! Cellular automata are simple rules that are applied to a grid of cells, or the pixel values of an image. The same rule is applied to every cell, to determine its next state, based on the previous state of that cell and its neighboring cells. There are many interesting cellular automata rules, and they all look very different, with amazing animated dynamic effects. "Life" is a widely known cellular automata rule, but many other lesser known rules are much more interesting! I've written code to play with real time animated cellular automata, that applies a bunch of rules to different parts of an image at once. You can create and delete different rules, move them around and resize them, throw them so they bounce around and leave trails, overlap them to mix their effects together, zoom into the cells and scroll around, and draw into the cells in real time with a few simple painting tools.
##### The latest news on my "Aether" After Effects Plug-in!

42. Dr.Cell Cellular Automata Simulator
A tool for simulating uniform or nonuniform cellular automata for a variety of neighborhood models, implemented in Scheme (a dialect of Lisp) using PLT s
http://student.vub.ac.be/~nkaraogl/drcell/drcell.htm
 Dr.Cell Cellular Automata Simulator Dr.Cell is a CA simulation tool implemented in Scheme programming language (a dialect of Lisp) using PLT's Dr.Scheme Dr.Cell allows you to simulate 1D, 2D, Uniform and Non-Uniform Cellular Automata graphically with user defined neighborhood models and rule sets. Following are a few samples that are implemented using Dr.Cell: You can load simulations using the Cellular Universe Control Center and execute multiple simulations at the same time. For each simulation there will be an individual window (Cellular World) showing the graphic representation of the simulation along with the cell statistics. Once a cellular world is created you can add more "automata" (Artificial Life Forms) on to the world and see their interactions. For example you can create a world for "Carrots" containing a specific rule set for the "Carrots". Then you may define another life-form "Rabbits" with the rule set for rabbits and add it to the "carrot world" and see how carrot and rabbit population evaluating in time. Dr.Cell allows you to adjust the speed of a simulation. You can also stop a simulation at any given time and execute it step by step.

43. Cellular Automata And Complex Systems
My gear for complexity research is a Java program of a 2dimensional cellular automata tool, called Cambria (was called CADemo). As you see below,
http://www001.upp.so-net.ne.jp/suzudo/index_e.html
##### Complex Systems and ALife Created by Cellular Automata (Java applets and many related links) by Tomoaki SUZUDO (Japanese is here)
access counter:
Java Applets
• : A newly added information
• : A cellular automaton demo using applets
##### What's new?
• New conference
ACRI 2006
Seventh International conference on Cellular Automata for Research and Industry, September 20-23, 2006
My gear for complexity research is a Java program of a 2-dimensional cellular automata tool, called Cambria (was called CADemo). As you see below, anyone can download my program including source codes. If you have a problem to decompress this file or want another compression format, please contact the Webmaster
• 44. Cellular Automata Applications
Cluster shape modeling (Y.Takai, T.Saito, Y.Tomikawa, and N.K.Takai A Particle Cluster Deformation Model by the Probabilistic Cellular Automata ,
##### Graphics Applications of Cellular Automata

45. RULE 30, RULE 30
A small 8bit cellular automata generator, based on the work of Stephen Wolfram. 8-BIT cellular automata. This small (81x40) 8B-CA generator is based on
http://www.stewdio.org/automata/
 var winWidth = 460; // a mere guess. self.resizeTo(winWidth, screen.availHeight); self.moveTo(((screen.availWidth / 2) - ((winWidth) / 2)), 0); RULE 30 is a good example of INHERENT RANDOM BEHAVIOR RULE 90 is a good example of NESTING RULE 30 8-BIT CELLULAR AUTOMATA This small (81x40) 8B-CA generator is based on work presented by Stephen Wolfram in his book A New Kind of Science . It demonstrates the ability of certain simple systems to manifest great complexity. For instance RULE 30 creates areas of texture that do not repeat and cannot be predicted ahead of the execution of the rule. Wolfram argues that this behavior is the essence of randomness. (This is similar to claiming that the distribution of prime numbers is random although numbers themselves do not spontaneously change between prime and composite.) Each RULE consists of 8 outcomes, one for each of the 8 possible states of 3 consecutive ON/OFF cells. The 8 outcomes of a rule are either ON (1) or OFF (0), yielding an 8 digit binary number (8 bits) and 256 possible rules (0 to 255 inclusive). For the execution of a rule each cell's immediate predecessor and predecessor's neighbors are examined (3 consecutive cells). The rule dictates what the outcome will be for each of the 8 possible states of 3 consecutive cells. This is applied to each cell, row by row. See more projects at stewdio.org

46. Automata - Agents Of Life Within
Introduction to cellular automata and other types including the Game of Life, and their applicability to artificial life, nanotechnology, mind and society.
http://www.calresco.org/automata.htm
##### by Chris Lucas
"On mechanical slavery, on the slavery of the machine,
the future of the world depends."
Oscar Wilde, The Soul of Man Under Socialism, 1895
##### Introduction
In this introduction we will look at some building blocks of organisation, both in terms of life (real and artificial) and in the structure of inorganic materials. We will investigate Cellular Automata and relate these to computers, brains and cells, plus speculate on the future goals of nanotechnology.
##### Understanding Automata
Firstly what exactly is an automaton ? Most people will recognise the word as one applied to a mechanical toy that emulates some apparently living behaviour. Within our field this is generalised to any system that has a finite number of internal states and moves between those states by following specified rules - this is a form of mapping (input to output, similar to a computer program). An automaton is also an agent in ALife terms, although agents can also occur in many other forms. An agent is an entity that can interact with its surroundings and usually changes its own state as a result. If we bring together a collection of such agents and allow them to interact then we have an automata system.
##### Cellular Automata
If we assume automata to be fixed (not mobile) we can equate them with cells in a structure. This structure could be living, molecular, mechanical - any form in fact. This gives us a Cellular Automata (CA), a structure that, whilst static in physical form and space, can exhibit dynamic behaviour in time -

47. Cellular Automata Knitting With Perl
In knitting we use rulebased 1-D cellular automata as a design tool. Like knitted fabric, these are composed of discrete units and grow in one direction.
http://chicago.pm.org/meetings/20040706/liz-1.html
##### What is knitting?
Knitting is a means of making fabric by pulling loops of yarn through other loops.
Loops, called 'stitches,' are the discrete unit of knitted fabric. Knitted fabric, worked in rows or spirals, grows in one direction.
##### What is a cellular automaton?
"A regular spatial lattice of "cells", each of which can have any one of a finite number of states... Each cell in a cellular automaton ... takes its neighbours' states as input and outputs its own state." http://computing-dictionary.thefreedictionary.com/cellular%20automaton
##### What do CA have to do with knitting?
In knitting we use rule-based 1-D cellular automata as a design tool. Like knitted fabric, these are composed of discrete units and grow in one direction. The state of each stitch's neighbors (typically its color) determines the state of the stitch. The rule is like a truth table with three inputs (stitch directly below, stitch below left, stitch below right) and an output. The diagram shows a rule in which three white stitches determine a black stitch, and all other combinations, a white stitch.
Read from left to right, the binary number created by the outputs is 00000001, or 1. Such rules can be summarized as a number from to 255

48. Levitated | Cellular Automata Worm | Flash MX Open Source
Natural continuous form through cellular automata. Levitated Source is Daily Computational Nutrition providing essential generative, mathematical,
http://www.levitated.net/daily/levCAWorm.html
 The CA Worm is an expanding construction of a one dimensional cellular automata. Each successive row of the automata is rotated and enlarged just slightly from the last. The result is a worm-like form, interestingly textured with a bitwise logic. CLICK anywhere above to change the CA ruleset and restart the worm growth. figure a. some example rulesets from Wolfram's 8-bit cellular automata method (with initial random conditions). Cellular automata are discreet dynamical systems with simple construction but complex emergent behavior. This particular implementation of CA is based on Wolfram's 8-bit Class 4 CA, using ruleset 114. The bits lit on each successive line of the CA are based on the lit bits of the previous lines. A similar cellular automata exists as a continually evolving interactive ring jtarbell , july 2003

49. Cellular Automata
From a historical perspective it s worth noting that cellular automata (CA) have been in widespread use for many decades as a means of simulating complex
http://www.mathpages.com/home/kmath416/kmath416.htm
 Cellular Automata Ever since digital computers became available, people have been fascinated by the behavior of discrete fields under the action of iterative algorithms. A discrete field f (j,t) consists of n ordered "locations", indexed by the integer j, at a sequence of discrete "times" t = 0, 1, 2, ...etc. Values f (j,0) are assigned to each of the locations at the initial time t = 0, and the values at subsequent times are computed recursively by the n functions for j = 0, 2,..., n-1. Thus the value at each location is a (possibly unique) function of the field values at all the locations for the preceding instant. However, in most studies the range of functions is greatly restricted so as to conform with the conventional preference for locality and homogeneity. According to the principle of locality, each F j should be a function of just those values of f j should be identical when expressed in terms of spatial offsets from the jth location. Homogeneity also suggests that either the locations extend infinitely in all directions, or else the dimensions are closed (e.g., cyclical), so that (in either case) there is no preferred location. From a historical perspective it's worth noting that "cellular automata" (CA) have been in widespread use for many decades as a means of simulating complex physical phenomena. For example, to determine the potential flow field around an airfoil (such as a wing) for irrotational flow of an incompressible inviscid fluid in two dimensions, we express the Laplace equation

50. Fractal Geometry
cellular automata (CA) are abstract mathematical machines inspired by the question Some cellular automata produce patterns whose time record is fractal;
http://classes.yale.edu/fractals/CA/welcome.html
##### 4. Cellular Automata and Fractal Evolution
Cellular automata (CA) are abstract mathematical machines inspired by the question "Can a machine build a copy of itself?" Defined by very simple rules, CA can produce a considerable variety of behaviors, including some that appear organic, and also many that are fractal. CA are a convenient setting for exploring genetic algorithms, a powerful computer science application of a major biological paradigm. Some of the patterns that apppear here also arise in music and in history, pointing to fractal aspects in those fields. Contents of this page: A The Paradox of Self-Replicating Machines Can a machine make a copy of itself? Before the mechanism for the replication of DNA was discovered, some thought self-reproduction could not be explained mechanically. Von Neumann's resolution of this problem led to the field of Artificial Life. B Cellular Automata Basics What are CA? How do they work? How do we build a simple universe in a computer? C Examples of Cellular Automata Patterns Yet another way to grow gaskets, and other surprises. Some cellular automata produce patterns whose time record is fractal; some others exhibit even more complicated behavior.

51. Moshe Sipper, A Brief Introduction To Cellular Automata
cellular automata (CA) were originally conceived by Ulam and von Neumann in the 1940s to provide a formal framework for investigating the behavior of
http://www.cs.bgu.ac.il/~sipper/ca.html

52. ACRI 2004
cellular automata, in spite of their apparent simplicity represent a very John von Neumann, who is recognized as the father of cellular automata,
http://www.science.uva.nl/research/scs/events/ACRI2004/
From individual to collective behaviour Sixth International conference on
Cellular Automata for Research and Industry
October 25-27, 2004
• Scope Local organising
committee
Invited speakers ...
• ##### News : movies now online !
Date:
Location:
October 25 - 27, 2004
University of Amsterdam, Science Park Amsterdam, The Netherlands Scope of the conference: Cellular Automata, in spite of their apparent simplicity represent a very powerful approach to study spatio-temporal systems in which complex phenomena build up out of many simple local interactions. They often provide solutions to real problems for which other, conventional approaches fail. John von Neumann, who is recognized as the father of cellular automata, would have been a hundred years old in 2004. ACRI 2004 wants to commemorate this important date by inviting researchers to submit contributions related to von Neumann's work or to the emergence of organisation in systems in which collaboration between components wins over the individual behaviour. The goal of this conference is to collect contributions concerning Cellular Automata in various fields such as theory, implementations and applications.

 53. Cellular Automata Machines - The MIT Press Recently, cellular automata machines with the size, speed, and flexibility for general experimentation at a moderate cost have become available to thehttp://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7072

54. Home
titulo.jpg (18597 bytes). inte2.jpg (272529 bytes). Working Papers Publications cellular automata Lectures Theses Summer Research
http://cellular.ci.ulsa.mx/oldweb/default.html
##### Theses ...
here to send mail to jmgomez@cs.cinvestav.mx

55. Caos
A Java applet running onedimensional cellular automata; by Martin Schaller.
http://members.surfeu.at/tim2/caos/caos.html
alt="Your browser understands the APPLET> tag but isn't running the applet, for some reason." Your browser is completely ignoring the APPLET> tag!
##### caos
One-Dimensional Cellular Automaton Simulation
a Java 1.1 capable browser is required to see the applet alt="Your browser understands the APPLET> tag but isn't running the applet, for some reason." Your browser is completely ignoring the APPLET> tag! starts caos in a resizable window description

rule

source
... X

56. Fields Institute - Automata 2007
Automata 2007 is thirteenth workshop in a series of AUTOMATA workshops established in 1995 by members of the Working Group 1.5 (cellular automata and
http://www.fields.utoronto.ca/programs/scientific/07-08/automata07/
Home About Us NPCDS/PNSDC Mathematics Education ... Search
##### SCIENTIFIC PROGRAMS AND ACTIVITIES
March 14, 2008
##### August 27-29, 2007 The Fields Institute
Organizers: Anna Lawniczak (University of Guelph) and (Brock University)
Scientific Program Committee:
Anna Lawniczak
(University of Guelph, Canada), (Brock University, Canada), Andrew Adamatzky (University of the West of England,UK), Danuta Makowiec (University of Gdansk, Poland) and Thomas Worsch (University of Karlsruhe, Germany)
Audio and slides of talks
Program Short Version Program Full Version Invited Speaker Abstracts ... Visitor Information Mailing List : To receive updates on activities at Fields please subscribe to our mailing list at www.fields.utoronto.ca/maillist
##### Overview
Automata 2007 is thirteenth workshop in a series of AUTOMATA workshops established in 1995 by members of the Working Group 1.5 (Cellular Automata and Machines) subordinated to Technical Committee 1 (Foundations of Computer Science) of the International Federation for Information Processing (IFIP). The main goal of AUTOMATA workshops is to maintain a permanent, international and multidisciplinary forum for the collaboration of researchers in the fields of Cellular Automata (CA) and Discrete Complex Systems (DCS).

57. Cellular Automata -- Britannica Online Encyclopedia
Furthermore, since they are twodimensional grids of cells, they can readily be programmed as cellular automata, systems of cells whose state depends on the
http://www.britannica.com/eb/topic-862593/cellular-automata
##### Main
Aspects of this topic are discussed in the following places at Britannica.
##### Citations
MLA Style: cellular automata http://www.britannica.com/bps/topic/862593/cellular-automata APA Style: cellular automata . (2008). In http://www.britannica.com/bps/topic/862593/cellular-automata cellular automata Link to this article and share the full text with the readers of your Web site or blog-post.
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58. EvCA Project
In our work, we use genetic algorithms to evolve cellular automata to perform Please see the papers on evolving cellular automata with genetic
http://cse.ucdavis.edu/~evca/Projects/evca.html
##### How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions?
In our work, we use genetic algorithms to evolve cellular automata to perform computational tasks requiring global information processing. We then analyze both the evolutionary process and the emergent behaviors of the evolved cellular automata that give rise to their computational abilities. This way, we hope to find answers to the above question. This page presents a short introduction to the evolving cellular automata framework, including some results. Please see the papers on evolving cellular automata with genetic algorithms for more details about this project.
##### Genetic Algorithms
Genetic algorithms (GAs) are stochastic search methods inspired by biological evolution. They are also widely used as simple models of evolutionary processes. A GA maintains a population of "individuals", often encoded as bit strings (the "genetic" material). These individuals can, for example, represent candidate solutions to some given problem, or "agents" living in some artificial environment, or parameter values for an optimization problem, etc. Starting out with a random initial population, the GA then "evolves" this population of individuals by means of (artificial) selection and reproduction with variation as follows. First, each individual is assigned a fitness value. This fitness value reflects how well the candidate solution that this individual represent solves the given problem, or how well the agent this individual represents manages to make a living in its artificial world, etc. In other words, the "better" the individual is, the higher its fitness value will be. Individuals are then selected at random according to their fitness values, where a higher fitness (relative to the population average) means a higher chance of being selected (possibly multiple times). A new population of offspring individuals is then created by randomly "mating" the selected individuals. In this process, recombination operators like crossover (mixing the "genetic" material of the selected individuals) and mutation (randomly changing some of the "genetic" material of an individual) are applied to create offspring that are genetic variants of their "parents".

59. Trend And JTrend Information - ISU Complex Computation Lab
jTrend is a Javabased cellular automata simulator modeled after Trend. This allows users to develop their cellular automata models using jTrend on a
 Trend and jTrend Information Download Trend Tutorials Trend Basics Game of Life One Dimensional CA Mouse Maze ... More Trend Examples This software is provided AS IS without user support due to lack of funding and the departure of our staff. 1. Trend Trend is a general purpose cellular automata simulation environment with an integrated high level language compiler, a beautiful graphical user interface, and a fast, three stage cached simulation engine. This is the simulation system that was used to discover the first emergent self-replicating cellular automata rule set, and the first problem solving self-replication loop. Since its simulator is very flexible with regard to cellular space sizes, cell structures, neighborhood structures and cellular automata rules, Trend can simulate almost all one or two-dimensional cellular automata models. It also has a smart backtracking feature which simplifies rule set development a lot by allowing users to go back to a previous stage of simulation! With other advanced features, Trend is probably the most easy to use 2-dimensional cellular automata simulator. Why not download a copy of Trend below and play with it today? Please read the README file after you download it to get started.

60. Cellular Automata And Lattice Gases Authors/titles Recent Submissions
Title Coalescing cellular automata Synchronizing CA by Common Random Source and Varying Asynchronicity. Authors JeanBaptiste Rouquier, Michel Morvan
http://arxiv.org/list/nlin.CG/recent
##### arXiv.orgnlinnlin.CG
Search or Article-id Help Advanced search All papers Titles Authors Abstracts Full text Help pages
##### Authors and titles for recent submissions
[ total of 5 entries:
[ showing up to 25 entries per page: fewer more
##### Thu, 6 Mar 2008
arXiv:0802.3926 (cross-list from q-bio.MN) [ pdf
Title: Stochastic Network Model of Receptor Cross-Talk Predicts Anti-Angiogenic Effects Authors: Amy L. Bauer Trachette L. Jackson Yi Jiang Thimo Rohlf Comments: 17 pages, 4 figures, 1 table Subjects: Molecular Networks (q-bio.MN) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Cellular Automata and Lattice Gases (nlin.CG); Quantitative Methods (q-bio.QM)
##### Mon, 3 Mar 2008
arXiv:0802.4365 ps pdf other
Title: Construction of Reversible Lattice Molecular Automata Authors: Takayuki Nozawa Toshiyuki Kondo Comments: 28 pages, 18 figures Subjects: Cellular Automata and Lattice Gases (nlin.CG) ; Adaptation and Self-Organizing Systems (nlin.AO)
##### Wed, 27 Feb 2008

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