Reviews (16)

Good Theoretical GA Textbook
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.

Not for beginners
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!

An introduction and much more
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

Reviews (1)

Great for GA-centered research, doubtful otherwise
Genetic Algorithms, GAs, have had a brief flowering of successful application to optimization searches and their limitations have become apparent. One consequence is that a variety of alternative evolutionary computational approaches are being investigated. Another road, much less travelled, is to examine the core mechanisms of the GA concept and try to develop a second generation of improved algorithms. This is difficult work because of the very nature of the core building block theory as first proposed by John Holland. For true inovation,building blocks must be synthesized, evaluated, and combined in sucessive hierarchies, all without external intervention. David Goldberg, a stalwart Holland desciple, has been valiantly trying to extend Holland's main theorem, which applied to infinite populations and hypthetical spaces, to finite populations on real problems.

This book is actually a research monograph reporting on the results of this research. The title "The Design of Innovation" sets up a high level of expectation but the subtitle "lessons learned from and for competent GAs" is probably right. The book offers some useful insights into the internal workings of GAs and their implication for understanding true innovation. However, despite the introductory claim of an engineering approach, the book never gets around to actually showing practitioners how to apply the lessons, nor does it give direct evidence that they work as claimed (although references to recent papers which presumably demonstrate success are given).

It is perhaps ironic that the goal for GAs has been downgraded from "universal" (as first claimed by Holland) to "competent".
Goldberg's concentrates on GAs to the exclusion of other approaches that may be equally competent or even better. A further irony is the stunning admission that "for years GA practitioners have understood that commercial applications often require" combinations of GAs and other local search methods to obtain high-quality solutions in reasonable time. But if this is so, then maybe GAs aren't the best place to start in the first place.

Goldberg's ideas about the upcoming golden age of computational innovation in the last chapter are provocative. But the implication that we must await GA improvements for this to happen are a little off-putting.

Reviews (5)

A survey of what was new in 2002
This book was published in 2002 to provide a survey of the direction research had taken in the field of Genetic Programming. There is an explanation of what genetic programming is and how it is different from genetic algorithms in chapter 1(GP is a "generalization" of GA). Chapter 2 discusses the problems with the fitness landscape. Chapter 3 - 6 discusses various schema theory approaches and proofs. Chapter 6 has a great explanation of effective fitness.

There are numerous theorems and proofs in the book. There are informative examples of the max problem and the artificial ant (Santa Fe Trail) problems. Chapter 11 is about how GP convergences are a tricky matter and how subtrees can hide interesting incidences of convergence.

This is not an introductory text, it is intended for graduate level or higher readers. There is much theoretical work here and a limited background in this area will result in limited understanding of the material.

The modern revolution
Currently working as an undergraduate student in Ann Arbor, Michigan as a Computer Science major I'm an intrigued by Genetic Programming alongside all motives of this in-depth field. I found this book to be a modest account of what is new and theoretical within this field. Expressing advanced features with a short introduction; this book is profoundly for somebody with somewhat of a background. A recommended start in the computer evolutionary field is:
An Introduction to Genetic Algorithms [1996], by Melanie Mitchell.

Reviews (6)

The essential reference for GP
Yeah, its a big book...weighs a ton.However, only the first few chapters are concerned with the basic mechanisms of GP (should be familiar to anyone with a background in genetic algorithms or evolutionary computation).The rest of the book is chock full of examples on how to apply GP.These examples are essential and very welcome.I've found that I can usually find a solved problem in Koza that is similar to what I'm after, then I adapt it to my needs.This is a great reference, but don't be fooled into thinking this book is a tutorial.Think of it more as an exposition of GP with examples.For a tutorial, look somewhere else.

Genetic Programming
The book was very large but enjoyable and made the subject very clear and easy to understand.It explained the genetic programming algorithm very well and showed the results of many experiments to show applicability, limitations, and characteristics of the method.

There was some repetition in places, maybe because the author wanted to emphasize some points and also to remain understandable to persons who may read selected chapters or examples rather than from cover to cover, page by page.

Although the book states that Genetic Programming does not depend on the LISP language or features, it uses LISP as its exclusive language of choice.I would like to implement these generally very computationally intensive Genetic Programming Algorithms in a very fast and efficient way, which for me implies assembly language, and although the author gives good tips about making the algorithm run faster the implementation shown is all LISP and nothing else.I am also interested in using the algorithm to generate efficient, parsimonious, code.The author described the additional problems of parsimony, but gave no information on generation of fast code from S expressions.I will have to refer to some compiler books and my own experiments to go further in this area.

I look forward to experimenting with the subject and reading some of Dr. Koza's other books on the subject.

Editorial Review

Reviews (2)

Excellent reference
The first volume provides a very broad coverage of the "evolutionary" literature. Reading this first volume will probably save you a lot of time. The evolutionary literature actually becomes quite large these days. The focus of this first volume is on broad coverage, not details although some chapters are already quite advanced.

If you need a fast coverage of the literature in evolutionary computation, this is the book. Pointers to all decisive contributions to the field are there. Reading from cover to cover might be difficult if the purpose is to introduce one to the field, but this is certainly the reference i would suggest to students and researchers new in this field. Each chapter is self-contained and references to the most important works for each chapter is provided at the end of the chapter.

More trouble with publisher than author
Overall, this and the second volume combined do well to cover the major topics of evolutionary computation. Unfortunately, the IOP (the publisher) is not very good making the books (especially the first volume) available. I used both volumes for a course I teach in evolutionary computation. I am one week from the end of the semester, and most of my students received volume 2, but are STILL waiting for volume 1.

Reviews (2)

One of the best books on EAs
Although this book is much less popular than Goldberg's and Mitchell's, it is the most complete reference on evolutionary algorithms in my opinion.If you're looking only for an introduction to EAs, this may not be the perfect book for you (the 2 other ones are more concise) but if you're seeking a detailed review of foundations of EAs, this book is excellent.It provides mathematical insight, and examples of how to implement such algorithms.

Isbn: 0792374525
Sales Rank: 1534615
Subjects:  1. Artificial Intelligence - General    2. Computers    3. Fuzzy algorithms    4. Fuzzy logic    5. Fuzzy systems    6. Game Theory    7. General    8. Genetic Algorithms    9. Linear Programming    10. Mathematical optimization    11. Mathematics    12. Optimization (Mathematical Theory)    13. Science/Mathematics    14. Mathematics / Linear Programming   

Reviews (5)

Different opinion
I haven't read this book yet (hence the intermediate 3 stars rating), but I think people should know that, [...] See e.g. http://www.techbookreport.com/tbr0031.html

Don't be fooled by the pompous title !
I completely agree with the previous reviewers. In fact, I found their criticism rather light. On the back cover, the book is presented as an introductory book with pratical emphasis. Although the book is very easy to follow (reading it cover to cover takes about 2-3 hours), it's definitely not at an introductory level. It covers basic material on GA & GP without sufficient detail. Furthermore, the design of the book is terrible. The author has allocated 105 pages to bibliography and appendices (the book has 225 pages). Therefore, the core material occupies only 120 pages. Moreover, the 2 chapters about GA and GP are totally 30 pages long ! The remaining 90 pages are about some not-so-interesting applications and future directions in EC. The bibliography is not at the end of the book. This makes following the references rather frustrating.

I've read/browsed at least 10 books on GA/EC. This is definitely the worst one. I recommend Eiben & Smith's "Introduction to Evolutionary Computing" and Michalewicz & Fogel's "How to Solve It: Modern Heuristics" to everyone interested in GA/EC.

Editorial Review

It might be simple, but it's not easy. Computer scientist Michael D. Vose takes a rigorous look at The Simple Genetic Algorithm and shows the state of our knowledge in a book appropriate for advanced undergraduates, graduate students, and professionals.

Vose has decided to approach his subject as a mathematical object, keeping his discussion to a minimum and relying on mathematical demonstrations of what has been proven about this powerful genetic search. This approach maximizes the book's utility for its scope of readers; since each chapter builds on the material before, it makes a good teaching tool, but it is still a useful reference as the indexing helps the professional find proofs quickly.