An Introduction to Genetic Algorithms (Complex Adaptive Systems)

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9780262631853: An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.

The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.

An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

About the Author :

Melanie Mitchell is Professor of Computer Science at Portland State University and External Professor at the Santa Fe Institute

Review :

Genetic algorithms (GAs) are of increasing interest, both as computational models of natural systems and as algorithmic techniques for problem-solving. This text fills an important role for student interested in Gas for either reason. It coverage of recent, theoretical GA work also helps to build a common foundation for the biologists and computer scientists intrigued by what Gas have to offer.

(Richard K. Belew, Associate Professor, Computer Science & Engineering Department, University of California, San Diego)

Melanie Mitchell has written an outstanding -- and needed -- new text for the burgeoning field for genetic algorithms. The book combines a clear explanation of GA basics and implementation, penetrating discussions of 15 of the most significant recent GA researches in problem solving and scientific modelling, and the first major review of GA theory from Holland's original concepts to recent advances and controversies. There are over 200 references. Mitchell's work is sure to become the field's new standard source and text.

(Stewart W. Wilson, The Rowland Institute for Science)

This is the best general book on Genetic algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic algorithms in scientific models; and it gives a good account of the status of the theory of Genetic algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.

(John H. Holland, Professor, Computer Science and Engineering,and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute)

Melanie Mitchell has written an excellent introduction to genetic algorithms, one of the most promising branches of machine learning. GAs, with their minimal demands on the programmer together with heavy exploitation of computing cycles, and uniquely well-positioned to take advantage of the vastly increased availability of computer cycles made covering both traditional GA methods and the recent wealth of GA variants, and also providing details on GA implementation, theoretical foundations, and scientific applications.

(David L. Waltz, NEC Research Institute and Brandeis University)

Melanie Mitchel has successfully assembled a collection of recent applications that convey the excitement and potential of genetic algorithms in solving an array of otherwise difficult or intractable problems.

(John R. Koza, Consulting Professor, Computer Science Department, Stanford University)

An outstanding introduction to a new and important field of computer science.

(Tim Watson The Computer Journal)

This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.

(Chris Robbins Computing)

This is the best general book on Genetic algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book.

(John H. Holland, Professor, Computer Science and Engineering,and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute)

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Description du livre MIT Press Ltd, United States, 1998. Paperback. État : New. Reprint. Language: English . Brand New Book. Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader s understanding of the text.The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation. N° de réf. du libraire AAH9780262631853

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Description du livre MIT Press Ltd, United States, 1998. Paperback. État : New. Reprint. Language: English . Brand New Book. Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader s understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation. N° de réf. du libraire AAH9780262631853

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Description du livre MIT Press Ltd, United States, 1998. Paperback. État : New. Reprint. Language: English . This book usually ship within 10-15 business days and we will endeavor to dispatch orders quicker than this where possible. Brand New Book. Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader s understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation. N° de réf. du libraire BTE9780262631853

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