A 1D cellular automaton that moves particles until regular spatial placement

International audienceWe consider a finite cellular automaton with particles where each site can host at most one particle. Starting from an arbitrary initial configuration, our goal is to move the particles between neighbor sites until the distance to the nearest particle is minimized globally. Such a configuration corresponds in fact to a regular placement. This problem is a cellular automata equivalent of load-balancing in parallel computing, where each task is a particle and each processor a connected set of sites. We present a cellular automata rule that solves this problem in the 1D case, and is convergent, i.e. once the regular placement is achieved, the configuration does not change anymore. The rule is inspired from the Lloyd algorithm, computing a centroidal Voronoi tessellation. The dynamic of the rule is described at a higher level, using self-explanatory space-time diagrams. They exhibit signals acting as quantity of movement carrying the energy of system. Each signal bounces or pass through particles, causing their movement, until it eventually reaches the border and vanishes. When signals have all vanished, particles are regularly placed

A 1D cellular automaton that moves particles until regular spatial placement

International audienceWe consider a finite cellular automaton with particles where each site can host at most one particle. Starting from an arbitrary initial configuration, our goal is to move the particles between neighbor sites until the distance to the nearest particle is minimized globally. Such a configuration corresponds in fact to a regular placement. This problem is a cellular automata equivalent of load-balancing in parallel computing, where each task is a particle and each processor a connected set of sites. We present a cellular automata rule that solves this problem in the 1D case, and is convergent, i.e. once the regular placement is achieved, the configuration does not change anymore. The rule is inspired from the Lloyd algorithm, computing a centroidal Voronoi tessellation. The dynamic of the rule is described at a higher level, using self-explanatory space-time diagrams. They exhibit signals acting as quantity of movement carrying the energy of system. Each signal bounces or pass through particles, causing their movement, until it eventually reaches the border and vanishes. When signals have all vanished, particles are regularly placed

Common metrics for cellular automata models of complex systems

The creation and use of models is critical not only to the scientific process, but also to life in general. Selected features of a system are abstracted into a model that can then be used to gain knowledge of the workings of the observed system and even anticipate its future behaviour. A key feature of the modelling process is the identification of commonality. This allows previous experience of one model to be used in a new or unfamiliar situation. This recognition of commonality between models allows standards to be formed, especially in areas such as measurement. How everyday physical objects are measured is built on an ingrained acceptance of their underlying commonality. Complex systems, often with their layers of interwoven interactions, are harder to model and, therefore, to measure and predict. Indeed, the inability to compute and model a complex system, except at a localised and temporal level, can be seen as one of its defining attributes. The establishing of commonality between complex systems provides the opportunity to find common metrics. This work looks at two dimensional cellular automata, which are widely used as a simple modelling tool for a variety of systems. This has led to a very diverse range of systems using a common modelling environment based on a lattice of cells. This provides a possible common link between systems using cellular automata that could be exploited to find a common metric that provided information on a diverse range of systems. An enhancement of a categorisation of cellular automata model types used for biological studies is proposed and expanded to include other disciplines. The thesis outlines a new metric, the C-Value, created by the author. This metric, based on the connectedness of the active elements on the cellular automata grid, is then tested with three models built to represent three of the four categories of cellular automata model types. The results show that the new C-Value provides a good indicator of the gathering of active cells on a grid into a single, compact cluster and of indicating, when correlated with the mean density of active cells on the lattice, that their distribution is random. This provides a range to define the disordered and ordered state of a grid. The use of the C-Value in a localised context shows potential for identifying patterns of clusters on the grid