Evolution of Parallel Cellular Machines [electronic resource] : The Cellular Programming Approach / edited by Moshe Sipper.
Material type: TextSeries: Lecture Notes in Computer Science ; 1194Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1997Description: XIII, 202 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540683407Subject(s): Computer science | Artificial intelligence | Biology -- Data processing | Computer Science | Computation by Abstract Devices | Processor Architectures | Programming Techniques | Artificial Intelligence (incl. Robotics) | Computer Appl. in Life SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 004.0151 LOC classification: QA75.5-76.95Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK6970 |
Universal computation in quasi-uniform cellular automata -- Studying artificial life using a simple, general cellular model -- Cellular programming: coevolving cellular computation -- Toward applications of cellular programming -- Online autonomous evolware: The firefly machine -- Studying fault tolerance in evolved cellular machines -- Coevolving architectures for cellular machines -- Concluding remarks and future research.
Collective systems, abounding in nature, have evolved by natural selection to exhibit striking problem-solving capacities. Employing simple yet versatile parallel cellular models, coupled with evolutionary computation techniques, this volume explores the issue of constructing man-made systems that exhibit characteristics like those occuring in nature. Parallel cellular machines hold potential both scientifically, as vehicles for studying phenomena of interest in areas such as complex adaptive systems and artificial life, and practically, enabling the construction of novel systems, endowed with evolutionary, reproductive, regenerative, and learning capabilities. This volume examines the behavior of such machines, the complex computation they exhibit, and the application of artificial evolution to attain such systems.
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