Издательство InTech, 2011, -580 pp.
In the early 1950s, at the suggestion of Stanislaw Ulam, John Von Neumann introduced the cellular automata as simple mathematical models to investigate self-organisation and self-reproduction. Cellular automata make up a very important class of completely discrete dynamical systems. The physical environment of cellular automata is constituted of a finite-dimensional lattice, with each site having a finite number of discrete states. The evolution in time of a cellular automaton goes on in discrete steps, and its dynamics is specified by some local transition rule, fixed and definite. In spite of their conceptual simplicity, which allows for an easiness of implementation for computer simulation, and a detailed and complete mathematical analysis in principle, the cellular automata systems are able to exhibit a wide variety of amazingly complex behavior. This feature of simplicity behind complexity of cellular automata has attracted the researchers’ attention from a wide range of divergent fields of study of science, which extends from the exact disciplines of mathematical physics up to the social ones, and beyond. In fact, nowadays, cellular automata are a core subject in the sciences of complexity. Thus, numerous complex systems containing many discrete elements with local interactions, and their complex collective behaviour which emerge from the interaction of a multitude of simple individuals, have been and are being conveniently modelled as cellular automata. For example, the dynamical Ising model, gas and fluid dynamics, traffic flow, various biological issues, growth of crystals, nonlinear chemical systems, land use and population phenomena and many others. Moreover, cellular automata are not the only models in natural sciences such as biology, chemistry and physics, but they are also, thanks to their complete space-time and state discreteness, appropriate models of parallel computation. Thus, cellular automata permit descriptions of natural processes in computational terms (computational biology, computational physics), but also of computation in biological and physical terms (artificial life, physics of computation).
In this book the versatility of cellular automata for modelling a wide diversity of complex systems is underlined through the study of a number of outstanding problems with the cellular automata innovative techniques. This book comprises twenty five contributions organized in four main sections: Land Use and Populations Dynamics; Dynamics of Traffic and Network Systems; Dynamics of Social and Economic Systems; and Statistical Physics and Complexity. Brief descriptions of the book chapters are presented in the following paragraphs.
Part 1 Land Use and Population Dynamics
An Interactive Method to Dynamically Create Transition Rules in a Land-use Cellular Automata Model
Cellular-Automata-Based Simulation of the Settlement Development in Vienna
Spatial Dynamic Modelling of Deforestation in the Amazon
Spatial Optimization and Resource Allocation in a Cellular Automata Framework
CA City: Simulating Urban Growth through the Application of Cellular Automata
Studies on Population Dynamics Using Cellular Automata
CA in Urban Systems and Ecology: From Individual Behaviour to Transport Equations and Population Dynamics
of Traffic and Network Systems
Equilibrium Properties of the Cellular Automata Models for Traffic Flow in a Single Lane
Cellular Automata for Traffic Modelling and Simulations in a Situation of Evacuation from Disaster Areas
Cellular Automata for Bus Dynamics
Application of Cellular Automaton Model to Advanced Information Feedback in Intelligent Transportation Systems
Network Systems Modelled by Complex Cellular Automata Paradigm
Cellular Automata Modeling of Biomolecular Networks
Simulation of Qualitative Peculiarities of Capillary System Regulation with Cellular Automata Models
Part 3 Dynamics of Social and Economic Systems
Social Simulation Based on Cellular Automata: Modeling Language Shifts
Cellular Automata Modelling of the Diffusion of Innovations
Cellular Automata based Artificial Financial Market
Some Results on Evolving Cellular Automata Applied to the Production Scheduling Problem
Part 4 Statistical Physics and Complexity
Nonequilibrium Phase Transition of Elementary Cellular Automata with a Single Conserved Quantity
Cellular Automata – a Tool for Disorder, Noise and Dissipation Investigations
Cellular Automata Simulation of Two-Layer Ising and Potts Models
Propositional Proof Complexity and Cellular Automata
Biophysical Modeling using Cellular Automata
Visual Spike Processing based on Cellular Automaton
Design and Implementation of CAOS: An Implicitly Parallel Language for the High-Performance Simulation of Cellular Automata
In the early 1950s, at the suggestion of Stanislaw Ulam, John Von Neumann introduced the cellular automata as simple mathematical models to investigate self-organisation and self-reproduction. Cellular automata make up a very important class of completely discrete dynamical systems. The physical environment of cellular automata is constituted of a finite-dimensional lattice, with each site having a finite number of discrete states. The evolution in time of a cellular automaton goes on in discrete steps, and its dynamics is specified by some local transition rule, fixed and definite. In spite of their conceptual simplicity, which allows for an easiness of implementation for computer simulation, and a detailed and complete mathematical analysis in principle, the cellular automata systems are able to exhibit a wide variety of amazingly complex behavior. This feature of simplicity behind complexity of cellular automata has attracted the researchers’ attention from a wide range of divergent fields of study of science, which extends from the exact disciplines of mathematical physics up to the social ones, and beyond. In fact, nowadays, cellular automata are a core subject in the sciences of complexity. Thus, numerous complex systems containing many discrete elements with local interactions, and their complex collective behaviour which emerge from the interaction of a multitude of simple individuals, have been and are being conveniently modelled as cellular automata. For example, the dynamical Ising model, gas and fluid dynamics, traffic flow, various biological issues, growth of crystals, nonlinear chemical systems, land use and population phenomena and many others. Moreover, cellular automata are not the only models in natural sciences such as biology, chemistry and physics, but they are also, thanks to their complete space-time and state discreteness, appropriate models of parallel computation. Thus, cellular automata permit descriptions of natural processes in computational terms (computational biology, computational physics), but also of computation in biological and physical terms (artificial life, physics of computation).
In this book the versatility of cellular automata for modelling a wide diversity of complex systems is underlined through the study of a number of outstanding problems with the cellular automata innovative techniques. This book comprises twenty five contributions organized in four main sections: Land Use and Populations Dynamics; Dynamics of Traffic and Network Systems; Dynamics of Social and Economic Systems; and Statistical Physics and Complexity. Brief descriptions of the book chapters are presented in the following paragraphs.
Part 1 Land Use and Population Dynamics
An Interactive Method to Dynamically Create Transition Rules in a Land-use Cellular Automata Model
Cellular-Automata-Based Simulation of the Settlement Development in Vienna
Spatial Dynamic Modelling of Deforestation in the Amazon
Spatial Optimization and Resource Allocation in a Cellular Automata Framework
CA City: Simulating Urban Growth through the Application of Cellular Automata
Studies on Population Dynamics Using Cellular Automata
CA in Urban Systems and Ecology: From Individual Behaviour to Transport Equations and Population Dynamics
of Traffic and Network Systems
Equilibrium Properties of the Cellular Automata Models for Traffic Flow in a Single Lane
Cellular Automata for Traffic Modelling and Simulations in a Situation of Evacuation from Disaster Areas
Cellular Automata for Bus Dynamics
Application of Cellular Automaton Model to Advanced Information Feedback in Intelligent Transportation Systems
Network Systems Modelled by Complex Cellular Automata Paradigm
Cellular Automata Modeling of Biomolecular Networks
Simulation of Qualitative Peculiarities of Capillary System Regulation with Cellular Automata Models
Part 3 Dynamics of Social and Economic Systems
Social Simulation Based on Cellular Automata: Modeling Language Shifts
Cellular Automata Modelling of the Diffusion of Innovations
Cellular Automata based Artificial Financial Market
Some Results on Evolving Cellular Automata Applied to the Production Scheduling Problem
Part 4 Statistical Physics and Complexity
Nonequilibrium Phase Transition of Elementary Cellular Automata with a Single Conserved Quantity
Cellular Automata – a Tool for Disorder, Noise and Dissipation Investigations
Cellular Automata Simulation of Two-Layer Ising and Potts Models
Propositional Proof Complexity and Cellular Automata
Biophysical Modeling using Cellular Automata
Visual Spike Processing based on Cellular Automaton
Design and Implementation of CAOS: An Implicitly Parallel Language for the High-Performance Simulation of Cellular Automata