The book offers an innovative approach to the study of discrete dynamical systems, such as one-dimensional and two-dimensional Cellular Automata (CA). The first section introduces recent work by the authors on the use of Genetic Algorithms to discover complex rules for one-dimensional CA. It goes on to show how these CA can generate novel structures with language-like properties. The second section provides a detailed discussion of two-dimensional Cellular Automata, beginning with von Neumann's initial proposal of an artificial self-replicating system. The authors show that self-replication is a common feature not only of biological but also of computational systems. They go on to show how it is possible to analyze computational self-replicators as if each system were a biological agent with its own genetic code. On this basis, they develop a taxonomy of self-replicators. The third part of the book shows how cellular automata can be used to generate sound and music, rendering some of the beauty of complexity. The authors present new techniques relevant to many important areas of CA research. More specifically, 1. they show how Genetic Algorithms can be used to detect complex rules both in one-dimensional and two-dimensional systems; 2. they create a formal language providing new insights into emerging CA structures (gliders and regular domains), and use sets of production rules to generate complex behavior ; 3. they describe the current state of the art in Boolean CA; 4. they provide a taxonomy of self-reproducing systems accessible to a wide-audience; 5. they demonstrate how cellular automaton can be seen as life-like agents, each with its own genetic code; 6. they evolve populations of complex agents; 7. they translate the output of cellular automata into sounds and music, using media to gain a better understanding of CA behavior; 8. they present a gallery of patterns generated by one and two-dimensional CA. The end product is a virtual laboratory which the authors use to explore the life-like processes set in motion by specific CA. Their simulations show how it is possible to change CA’s form and function by varying their “genome”. Their study of the relationship between the genome, structure and function of CA , provides new ways of interpreting evolutionary trajectories in CA parameter space and demonstrate CA power as a model of life-like phenomena.
Cellular Automata and Complex systems. Methods for Modeling Biological Phenomena
BILOTTA, Eleonora;PANTANO, Pietro Salvatore
2010-01-01
Abstract
The book offers an innovative approach to the study of discrete dynamical systems, such as one-dimensional and two-dimensional Cellular Automata (CA). The first section introduces recent work by the authors on the use of Genetic Algorithms to discover complex rules for one-dimensional CA. It goes on to show how these CA can generate novel structures with language-like properties. The second section provides a detailed discussion of two-dimensional Cellular Automata, beginning with von Neumann's initial proposal of an artificial self-replicating system. The authors show that self-replication is a common feature not only of biological but also of computational systems. They go on to show how it is possible to analyze computational self-replicators as if each system were a biological agent with its own genetic code. On this basis, they develop a taxonomy of self-replicators. The third part of the book shows how cellular automata can be used to generate sound and music, rendering some of the beauty of complexity. The authors present new techniques relevant to many important areas of CA research. More specifically, 1. they show how Genetic Algorithms can be used to detect complex rules both in one-dimensional and two-dimensional systems; 2. they create a formal language providing new insights into emerging CA structures (gliders and regular domains), and use sets of production rules to generate complex behavior ; 3. they describe the current state of the art in Boolean CA; 4. they provide a taxonomy of self-reproducing systems accessible to a wide-audience; 5. they demonstrate how cellular automaton can be seen as life-like agents, each with its own genetic code; 6. they evolve populations of complex agents; 7. they translate the output of cellular automata into sounds and music, using media to gain a better understanding of CA behavior; 8. they present a gallery of patterns generated by one and two-dimensional CA. The end product is a virtual laboratory which the authors use to explore the life-like processes set in motion by specific CA. Their simulations show how it is possible to change CA’s form and function by varying their “genome”. Their study of the relationship between the genome, structure and function of CA , provides new ways of interpreting evolutionary trajectories in CA parameter space and demonstrate CA power as a model of life-like phenomena.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
