Simulating Complex Systems by Cellular Automata [electronic resource] /
Hoekstra, Alfons G.editor.
Kroc, Jiri.editor.
Sloot, Peter M.A.editor.
Deeply rooted in fundamental research in Mathematics and Computer Science, Cellular Automata (CA) are recognized as an intuitive modeling paradigm for Complex Systems. Already very basic CA, with extremely simple micro dynamics such as the Game of Life, show an almost endless display of complex emergent behavior. Conversely, CA can also be designed to produce a desired emergent behavior, using either theoretical methodologies or evolutionary techniques. Meanwhile, beyond the original realm of applications - Physics, Computer Science, and Mathematics – CA have also become work horses in very different disciplines such as epidemiology, immunology, sociology, and finance. In this context of fast and impressive progress, spurred further by the enormous attraction these topics have on students, this book emerges as a welcome overview of the field for its practitioners, as well as a good starting point for detailed study on the graduate and post-graduate level. The book contains three parts, two major parts on theory and applications, and a smaller part on software. The theory part contains fundamental chapters on how to design and/or apply CA for many different areas. In the applications part a number of representative examples of really using CA in a broad range of disciplines is provided - this part will give the reader a good idea of the real strength of this kind of modeling as well as the incentive to apply CA in their own field of study.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references.
to Modeling of Complex Systems Using Cellular Automata -- Theory of Cellular Automata -- Multilevel Cellular Automata as a Tool for Studying Bioinformatic Processes -- Complex Automata: Multi-scale Modeling with Coupled Cellular Automata -- Hierarchical Cellular Automata Methods -- Cellular Automata Composition Techniques for Spatial Dynamics Simulation -- Problem Solving on One-Bit-Communication Cellular Automata -- Minimal Cellular Automaton Model of Inter-species Interactions: Phenomenology, Complexity and Interpretations -- Cellular Evolutionary Algorithms -- Artificial Evolution of Arbitrary Self-Replicating Structures in Cellular Spaces -- Applications of Cellular Automata -- Game Theoretical Interactions of Moving Agents -- Lattice Boltzmann Simulations of Wetting and Drop Dynamics -- CA Modeling of Ant-Traffic on Trails -- Lattice-Gas Cellular Automaton Modeling of Emergent Behavior in Interacting Cell Populations -- Cellular Automata for Simultaneous Analysis and Optimal Structural Topology Design -- Cellular Automata Software -- Parallel Cellular Programming for Emergent Computation.
Deeply rooted in fundamental research in Mathematics and Computer Science, Cellular Automata (CA) are recognized as an intuitive modeling paradigm for Complex Systems. Already very basic CA, with extremely simple micro dynamics such as the Game of Life, show an almost endless display of complex emergent behavior. Conversely, CA can also be designed to produce a desired emergent behavior, using either theoretical methodologies or evolutionary techniques. Meanwhile, beyond the original realm of applications - Physics, Computer Science, and Mathematics – CA have also become work horses in very different disciplines such as epidemiology, immunology, sociology, and finance. In this context of fast and impressive progress, spurred further by the enormous attraction these topics have on students, this book emerges as a welcome overview of the field for its practitioners, as well as a good starting point for detailed study on the graduate and post-graduate level. The book contains three parts, two major parts on theory and applications, and a smaller part on software. The theory part contains fundamental chapters on how to design and/or apply CA for many different areas. In the applications part a number of representative examples of really using CA in a broad range of disciplines is provided - this part will give the reader a good idea of the real strength of this kind of modeling as well as the incentive to apply CA in their own field of study.
Computer simulation.
Statistical physics.
Dynamical systems.
Computer mathematics.
Computational intelligence.
Simulation and Modeling.
Complex Systems.
Computational Science and Engineering.
Computational Intelligence.
Statistical Physics and Dynamical Systems.