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12
Training cellular automata for image processing
 IEEE Transactions on Image Processing
"... Abstract. Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward sea ..."
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Abstract. Experiments were carried out to investigate the possibility of training cellular automata to to perform processing. Currently, only binary images are considered, but the space of rule sets is still very large. Various objective functions were considered, and sequential floating forward search used to select good rule sets for a range of tasks, namely: noise filtering, thinning, and convex hulls. Several modifications to the standard CA formulation were made (the Brule and 2cycle CAs) which were found to improve performance. 1
Using Shape Grammar to Derive Cellular Automata Rule Patterns
"... This paper shows how shape grammar can be used to derive cellular automata (CA) rules. Searching the potentially astronomical space of CA rules for relevance to a particular context has frustrated the wider application of CA as powerful computing systems. An approach is offered using shape grammar t ..."
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This paper shows how shape grammar can be used to derive cellular automata (CA) rules. Searching the potentially astronomical space of CA rules for relevance to a particular context has frustrated the wider application of CA as powerful computing systems. An approach is offered using shape grammar to visually depict the desired conditional rules of a behavior or system architecture (a formfunction) under investigation, followed by a transcription of these rules as patterns into CA. The combination of shape grammar for managing the input and CA for managing the output brings together the human intuitive approach (visualization of the abstract) with a computational system that can generate large design solution spaces in a tractable manner. 1.
Cryptography by Cellular Automata or How Fast Can Complexity Emerge in Nature?
"... Computation in the physical world is restricted by the following spatial locality constraint: In a single unit of time, information can only travel a bounded distance in space. A simple computational model which captures this constraint is a cellular automaton: A discrete dynamical system in which c ..."
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Computation in the physical world is restricted by the following spatial locality constraint: In a single unit of time, information can only travel a bounded distance in space. A simple computational model which captures this constraint is a cellular automaton: A discrete dynamical system in which cells are placed on a grid and the state of each cell is updated via a local deterministic rule that depends only on the few cells within its close neighborhood. Cellular automata are commonly used to model real world systems in nature and society. Cellular automata were shown to be capable of a highly complex behavior. However, it is not clear how fast this complexity can evolve and how common it is with respect to all possible initial configurations. We examine this question from a computational perspective, identifying “complexity ” with computational intractability. More concretely, we consider an ncell automaton with a random initial configuration, and study the minimal number of computation steps t = t(n) after which the following problems can become computationally hard: • The inversion problem. Given the configuration y at time t, find an initial configuration x which leads to y in t steps.
Brill Academic Publishers
, 2005
"... Complex binary sequences were generated by applying a simple threshold, linear transformation to the logistic iterative function, xn+1 = rxn (1xn). Depending primarily on the value of the nonlinearity parameter r, the logistic function exhibits a great variety of behavior, including stable states, ..."
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Complex binary sequences were generated by applying a simple threshold, linear transformation to the logistic iterative function, xn+1 = rxn (1xn). Depending primarily on the value of the nonlinearity parameter r, the logistic function exhibits a great variety of behavior, including stable states, cycling and periodical activity and the period doubling phenomenon that leads to highorder chaos. Binary sequences of length 2L were used in our computer experiments. The first L bits (first half) were given as input to Cellular Automata (CA) with the task to regenerate the remaining L bits (second half) of the binary sequence in less than L evolution steps of the CA. To perform this task a suitably designed Genetic Algorithm (GA) was developed for the evolution of CA rules. Various complex binary sequences were examined, for a variety of initial values of x0 and a wide range of the nonlinearity parameter, r.
Fast Rule Identification and Neighbourhood Selection for Cellular Automata
"... Abstract—Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both timeconsuming, and inefficient when selecting neighbourhoods. We give a novel a ..."
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Abstract—Cellular automata (CA) with given evolution rules have been widely investigated, but the inverse problem of extracting CA rules from observed data is less studied. Current CA rule extraction approaches are both timeconsuming, and inefficient when selecting neighbourhoods. We give a novel approach to identifying CA rules from observed data, and selecting CA neighbourhoods based on the identified CA model. Our identification algorithm uses a model linear in its parameters, and gives a unified framework for representing the identification problem for both deterministic and probabilistic cellular automata. Parameters are estimated based on a minimumvariance criterion. An incremental procedure is applied during CA identification to select an initial coarse neighbourhood. Redundant cells in the neighbourhood are then removed based on parameter estimates, and the neighbourhood size is determined using a Bayesian information criterion. Experimental results show the effectiveness of our algorithm, and that it outperforms other leading CA identification algorithms. Index Terms—Cellular automata, rule identification, neighbourhood selection. I.
Cycles, Transients, and Complexity in the Game of Death Spatial Automaton
"... Cellular automaton models such as Conway’s Game of Life have long been shown to exhibit a high degree of spatial complexity. Spatial patterns can be analysed and categorised in this and other models and used as a basis for cataloging other related models and their behaviour classes. An interesting v ..."
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Cellular automaton models such as Conway’s Game of Life have long been shown to exhibit a high degree of spatial complexity. Spatial patterns can be analysed and categorised in this and other models and used as a basis for cataloging other related models and their behaviour classes. An interesting variation arises when a third state is introduced and we explore the consequences of this in models like Silverman’s “Brian’s Brain ” – sometimes known as the “Game of Death ” where “zombies ” are introduced into the spatial model. The third state and the microscopic rules associated with the three constituent species gives rise to a rich new set of phases and behaviours with can be simulated and catalogued statistically. We focus on the early transient behaviour following a random system initialisation and the initial thinning out following by a subsequent explosion in species diversity. KEY WORDS multiagent model; cellular automata; biodiversity; cyclic states. 1
Author's personal copy ARTICLE IN PRESS
, 2007
"... www.elsevier.com/locate/yjtbi A viral loadbased cellular automata approach to modeling HIV dynamics and drug treatment ..."
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www.elsevier.com/locate/yjtbi A viral loadbased cellular automata approach to modeling HIV dynamics and drug treatment
Contents lists available at ScienceDirect Mathematical and Computer Modelling
"... journal homepage: www.elsevier.com/locate/mcm Evolving cellular automata rules for multiplestepahead prediction of ..."
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journal homepage: www.elsevier.com/locate/mcm Evolving cellular automata rules for multiplestepahead prediction of
Genetic Algorithm Evolution of Cellular Automata Rules for Complex Binary Sequence Prediction
"... Abstract: Complex binary sequences were generated by applying a simple threshold, linear transformation to the logistic iterative function, xn+1 = r xn (1−xn). Depending primarily on the value of the nonlinearity parameter r, the logistic function exhibits a great variety of behavior, including sta ..."
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Abstract: Complex binary sequences were generated by applying a simple threshold, linear transformation to the logistic iterative function, xn+1 = r xn (1−xn). Depending primarily on the value of the nonlinearity parameter r, the logistic function exhibits a great variety of behavior, including stable states, cycling and periodical activity and the period doubling phenomenon that leads to highorder chaos. Binary sequences of length 2L were used in our computer experiments. The first L bits (first half) were given as input to Cellular Automata (CA) with the task to regenerate the remaining L bits (second half) of the binary sequence in less than L evolution steps of the CA. To perform this task a suitably designed Genetic Algorithm (GA) was developed for the evolution of CA rules. Various complex binary sequences were examined, for a variety of initial values of x0 and a wide range of the nonlinearity parameter, r. The proposed hybrid prediction algorithm, based on a combination of GAs and CA proved quite efficient.