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Adaptation in Natural and Artificial Systems - An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence (Paperback, New Ed)
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Adaptation in Natural and Artificial Systems - An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence (Paperback, New Ed)
Series: Complex Adaptive Systems S.
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Genetic algorithms are playing an increasingly important role in
studies of complex adaptive systems, ranging from adaptive agents
in economic theory to the use of machine learning techniques in the
design of complex devices such as aircraft turbines and integrated
circuits. "Adaptation in Natural and Artificial Systems" is the
book that initiated this field of study, presenting the theoretical
foundations and exploring applications. In its most familiar form,
adaptation is a biological process, whereby organisms evolve by
rearranging genetic material to survive in environments confronting
them. In this now classic work, Holland presents a mathematical
model that allows for the nonlinearity of such complex
interactions. He demonstrates the model's universality by applying
it to economics, physiological psychology, game theory, and
artificial intelligence and then outlines the way in which this
approach modifies the traditional views of mathematical genetics.
Initially applying his concepts to simply defined artificial
systems with limited numbers of parameters, Holland goes on to
explore their use in the study of a wide range of complex,
naturally occuring processes, concentrating on systems having
multiple factors that interact in nonlinear ways. Along the way he
accounts for major effects of coadaptation and coevolution: the
emergence of building blocks, or schemata, that are recombined and
passed on to succeeding generations to provide, innovations and
improvements. John H. Holland is Professor of Psychology and
Professor of Electrical Engineering and Computer Science at the
University of Michigan. He is also Maxwell Professor at the Santa
Fe Institute and is Director of theUniversity of Michigan/Santa Fe
Institute Advanced Research Program.
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