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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.
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Most Helpful Customer Reviews
14 of 14 people found the following review helpful:
5.0 out of 5 stars
An extremely inspirational insight in GAs,
By A Customer
This review is from: An Introduction to Genetic Algorithms (Complex Adaptive Systems) (Hardcover)
From the very first page Mitchell has the reader hooked as she unleashes the various applications of GAs. With each example the various elements of GAs are illustrated, making it an enjoyable but still very educational experience. The technical material is not too hard to follow, and the mathematical proofs presented are easy to understand given a modest background in maths. I recommend this book as a first read for anyone who is interested in learning more about GAs, and an entertaining read for the GA veterans.
8 of 8 people found the following review helpful:
5.0 out of 5 stars
An excellent first contact with the subject.,
By
This review is from: An Introduction to Genetic Algorithms (Complex Adaptive Systems) (Paperback)
This is the book that launched my interest in GA's, it took a subject that I expected to bored to tears with and totally engaged me, I just couldn't put this book down. The authors style makes for easy reading but at the same time does not skimp on detail. A very good starting point for anyone looking to introduce themselves to the subject and jam packed with usefull references for those wishing to study further.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
Great introduction for the uninitiated!,
By A Customer
This review is from: An Introduction to Genetic Algorithms (Complex Adaptive Systems) (Hardcover)
This book is ideal for someone totally new to the field of GAs. Mitchell begins with the fundamental concepts of the simple GA and proceeds to survey a wide variety of applications. I especially enjoyed the coverage of topics related to machine intelligence, which are sometimes left out in books that focus solely on optimization. The book contains enough information for someone with programming experience to code their own GA (including suggested computer exercises), although no source code is presented. However, the background gained from reading Mitchell's book will enable an easier read of more technical books (which may include source code implementations).
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