PDF Download An Introduction to Genetic Algorithms (Complex Adaptive Systems), by Melanie Mitchell

PDF Download An Introduction to Genetic Algorithms (Complex Adaptive Systems), by Melanie Mitchell

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An Introduction to Genetic Algorithms (Complex Adaptive Systems), by Melanie Mitchell

PDF Download An Introduction to Genetic Algorithms (Complex Adaptive Systems), by Melanie Mitchell

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Review

"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.

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About the Author

Melanie Mitchell is Research Professor and Director of the Adaptive Computation Program at the Santa Fe Institute.

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Product details

Series: Complex Adaptive Systems

Hardcover: 224 pages

Publisher: The MIT Press (February 27, 1996)

Language: English

ISBN-10: 0262133164

ISBN-13: 978-0262133166

Product Dimensions:

7.5 x 0.8 x 10.5 inches

Shipping Weight: 1.6 pounds (View shipping rates and policies)

Average Customer Review:

3.5 out of 5 stars

16 customer reviews

Amazon Best Sellers Rank:

#1,786,119 in Books (See Top 100 in Books)

Melanie Mitchell’s book “an introduction to Genetic Algorithms” explains what Genetic Algorithms are and how they work. It is somewhat outdated by now. However, that does not matter a whole lot since the book is focused on the foundations and the theory behind genetic algorithms and is academic in nature. This book is not a “cook-book” for Genetic Algorithms, and it does not have any practical examples or code that you can “borrow”. Being an academic book it goes into the theoretical foundations of Genetic Algorithms, it uses a fair amount of mathematics, and it backs up claims and discussions with references to research articles. At times the mathematics gets a little bit complex and you better know something about probability, functions, matrix algebra, vector/matrix notation, and infinite series.For those who do not know; Genetic Algorithms imitate aspects of the evolutionary process observed in nature to solve engineering problems and scientific problems. As such it can also shed light on the natural evolutionary processes (punctuated equilibria, the Baldwin effect, etc). In Genetic Algorithms you have a genetic representation, for example, a “chromosome” (bit string) and you simulate cross over (chromosome blending), mutations, fitness criteria, etc. Melanie Mitchell describes the use of Genetic Algorithms in scientific models, and how they can be used to simulate and explain evolution in nature, she describes different approaches to Genetic Algorithms, automatic programming, using Genetic Algorithms for prediction, and she explains how to use them to solve problems in Artificial Intelligence/Computer Science, and she also describes how to use them with evolving Neural Networks.What I liked about the book is that despite the fact that it is only 200 pages it covers a lot of ground. The book is well organized, well written, interesting, and concise. I read this book because I was interested in finding out whether I could use some form of Genetic Algorithm to solve some optimization problems at work. Therefore it might not have been exactly the right book for me. At the same time I found the book to be quite interesting and I liked the learning experience. You should understand Genetic Algorithms before you use them anyway. The book is 16 years old by now, perhaps too academic for some people’s taste, and I believe I found a term that was left out in a derivation, so I’ll give a four star rating, but I enjoyed reading it.

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

I have an engineering degree, and I found this to be a little tough to follow for two reasons:1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

Thanks! Arrived as advertised.

Great.

This book is NOT an "Introduction" to Genetic Algorithms. It is an academia blathering about genetic algorithms citing mostly other people's work with no practical examples. If you are looking to actually get some program code up and running to experiment with genetic algorithms, this is NOT the book to get. If you are a professor searching for a GA book to use in your classes, please do not select this one. Your students will hate your class.However, if you are looking for a discussion of some variations of genetic algorithm approaches to solving various problems from a higher level perspective, it might be worth a read. I realize this review may seem harsh, but when a book is so completely mis-titled it is misleading. This book is written for people with a thorough knowledge of genetic algorithm use already.

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