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An Introduction to Genetic Algorithms
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.
221 pages, Paperback
First published January 1, 1996
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574 people want to read
About the author
Melanie Mitchell
17Ìýbooks204ÌýfollowersMelanie Mitchell is a professor of computer science at Portland State University. She has worked at the Santa Fe Institute and Los Alamos National Laboratory. Her major work has been in the areas of analogical reasoning, complex systems, genetic algorithms and cellular automata, and her publications in those fields are frequently cited.
She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception", essentially a book about Copycat. She has also critiqued Stephen Wolfram's A New Kind of Science and showed that genetic algorithms could find better solutions to the majority problem for one-dimensional cellular automata. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. She is also author of Complexity: A Guided Tour (Oxford University Press, 2009), which won the 2010 Phi Beta Kappa Science Book Award.
She received her PhD in 1990 from the University of Michigan under Douglas Hofstadter and John Holland, for which she developed the Copycat cognitive architecture. She is the author of "Analogy-Making as Perception", essentially a book about Copycat. She has also critiqued Stephen Wolfram's A New Kind of Science and showed that genetic algorithms could find better solutions to the majority problem for one-dimensional cellular automata. She is the author of An Introduction to Genetic Algorithms, a widely known introductory book published by MIT Press in 1996. She is also author of Complexity: A Guided Tour (Oxford University Press, 2009), which won the 2010 Phi Beta Kappa Science Book Award.
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Displaying 1 - 12 of 12 reviews
July 5, 2022
An overview of Genetic Algorithm theory and examples from the first few decades of research.
This book starts by explaining what Genetic Algorithms are, what they're used for, and how they perform. It explores selected examples of GAs for practical problem solving and scientific modeling, explaining the design and performance of each GA in detail and explaining what makes them interesting to the field in general. Lastly, the book explores general theory, common techniques, and important design decisions for implementing GAs. Unfortunately, the book was published in 1996 and has not been updated, so it doesn't cover any of the progress made in the field since then.
As someone interested in designing novel GAs, I found this book fascinating and tremendously useful. Mitchell does a great job of summarizing the enormous breadth and variation within this field. She strikes a good balance between describing published work in rigorous detail, and discussing the theoretical implications, value, shortcomings, and possible future directions of that work. Her enthusiasm for the field is clear, and it helps hold the reader's interest and keep the book flowing. This book is short and to the point, cherry-picking only a modest number of examples to discuss, and providing lots of leads for further research to the reader. It's rather technical, and expects the reader to have a solid background in Computer Science.
I'd highly recommend this book to anyone with a Computer Science education who wants to learn what Genetic Algorithms are all about.
This book does contain practice problems for the reader, but they weren't the focus and I didn't find them particularly useful. For a practical introduction to writing GAs to solve problems, I'd recommend a book like Genetic Algorithms with Python instead.
This book starts by explaining what Genetic Algorithms are, what they're used for, and how they perform. It explores selected examples of GAs for practical problem solving and scientific modeling, explaining the design and performance of each GA in detail and explaining what makes them interesting to the field in general. Lastly, the book explores general theory, common techniques, and important design decisions for implementing GAs. Unfortunately, the book was published in 1996 and has not been updated, so it doesn't cover any of the progress made in the field since then.
As someone interested in designing novel GAs, I found this book fascinating and tremendously useful. Mitchell does a great job of summarizing the enormous breadth and variation within this field. She strikes a good balance between describing published work in rigorous detail, and discussing the theoretical implications, value, shortcomings, and possible future directions of that work. Her enthusiasm for the field is clear, and it helps hold the reader's interest and keep the book flowing. This book is short and to the point, cherry-picking only a modest number of examples to discuss, and providing lots of leads for further research to the reader. It's rather technical, and expects the reader to have a solid background in Computer Science.
I'd highly recommend this book to anyone with a Computer Science education who wants to learn what Genetic Algorithms are all about.
This book does contain practice problems for the reader, but they weren't the focus and I didn't find them particularly useful. For a practical introduction to writing GAs to solve problems, I'd recommend a book like Genetic Algorithms with Python instead.
Want to read
June 22, 2020recommended: [EP33 Melanie Mitchell on the Elements of AI - The Jim Rutt Show]()
October 16, 2018
This is an introduction to genetic algorithms with case studies and a literature survey. It's 20 years old, so the survey is like a time capsule from the late 90s (I've no idea how much the GA world has moved on since then). But the introduction part is timeless, the exercises useful, and importantly the book is nice and short.
September 20, 2018
good readable introduction or refresher
July 13, 2016
This is the first book I've read on GAs. The impression that it makes is that the field of Genetic Algorithms is nascent (or at least was, at the time of writing the book). I was hoping for solid insights into various issues related to GAs, like which problems are best suited for a GA based solution, how to design a GA for a given problem, how to choose the various primitives for a GA, and how to select various parameters... However, I could find none of that. All that I saw was heuristics, heuristics and more heuristics. I cannot fault the book, for it is the nature of the subject, perhaps. I could see that GAs are a promising area, but complex and not fully understood. I think, a newer book would have more and recent examples.
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October 16, 2012Gran bella introduzione agli Algoritmi Genetici, buon bilanciamento fra esempi e teoria (due capitoli ciascuno). Inoltre, ottima parte finale con tutti i riferimenti per maggiori informazioni.
(PS: ormai un po' datato.)
(PS: ormai un po' datato.)
June 5, 2009
The best book around on one of the most fascinating subjects around. Contains everything you need to know to start writing genetic algorithms.
June 14, 2009
A great intro to the subject.
April 11, 2014
Good intro to the topic. Still relevant after all these years, that is commendable.
August 25, 2014
selective reading was useful
October 12, 2015
Genetic algorithms are very underwhelming. There should at least be a few captivating (nonbiological) examples, but this book presents very few successes.
Displaying 1 - 12 of 12 reviews
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