- Hardcover: 260 pages
- Publisher: Springer (14 Feb 2002)
- Language German
- ISBN-10: 3540424512
- ISBN-13: 978-3540424512
- Product Dimensions: 23.4 x 16.3 x 2.3 cm
- Average Customer Review: 3.5 out of 5 stars See all reviews (2 customer reviews)
- Amazon Bestsellers Rank: 1,763,386 in Books (See Top 100 in Books)
- See Complete Table of Contents
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Product Description
Review
From the reviews: I came to this book from an engineering perspective as a GP practitioner interested in practical issues such as which cross-over operator was most applicable for my problem. Whilst this book did not offer any clear-cut answers, this is a reflection of the fact that there are no clear-cut answers, yet. What the book does succeed in doing is providing an illuminating overview of the body of work which will, in time, come to provide a theoretical foundation, and accurate prescriptions, for all of the ad-hoc tweaks and adjustments that we make in practise. This was published in the British Computer Society journal "Expert Update", 5(3) p46, 2002 by Steve Phelps. "Is genetic programming (GP) better than random search? … Langdon and Poli take on the ambitious task of giving a unified overview of a field still in its infancy, and the result is an invaluable companion to the literature. The book … proceeds to give a comprehensive and illuminating treatment of the most important theorems. … throughout the book the formal side of the theory is developed alongside intuitive explanations and constructive analysis of actual empirical data." (Steve Phelps, Expert Update, Vol. 5 (3), 2002) "The book ‘Foundations of Genetics Programming’ summarizes appearances and approaches in the GP section. … There are many references for details in the text. Naturally, a large list of references is printed in the appendix. In conclusion, the book describes general principles of genetic programming. I recommend this as the first book for those who are familiarized with the GA and want to be in the know of the GP." (Vít Fábera, Neural Network World, Vol. 12 (4), 2002)
Product Description
This is one of the only books to provide a complete and coherent review of the theory of genetic programming (GP). In doing so, it provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.
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First Sentence
The idea of fitness landscapes, firstly proposed in genetics by Sewall Wright [Wright, 1932], is well established in artificial evolution. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
The idea of fitness landscapes, firstly proposed in genetics by Sewall Wright [Wright, 1932], is well established in artificial evolution. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most Helpful Customer Reviews
4 of 7 people found the following review helpful:
5.0 out of 5 stars
Excellent review of the state of the art,
By Stefano Cagnoni (Parma, Italy) - See all my reviews
This review is from: Foundations of Genetic Programming (Hardcover)
I have found this book very good and inspiring. This book is about theoretical and empirical studies on the fundamental question of how and why genetic programming works. In this respect the title of the book is very appropriate. The book should not be expected to be an introduction to genetic programming for beginners, as the author of another review seem to believe. With respect to that review, I also disagree with the statement that the book is just a collection of papers. While it is true that the results in the book have been published before, the authors have made an incredible effort to integrate those results into a single coherent picture, which I have enjoyed very much viewing. Not for beginners, but definitely cutting edge high quality research.
5 of 9 people found the following review helpful:
2.0 out of 5 stars
Not really that good,
By A Customer
This review is from: Foundations of Genetic Programming (Hardcover)
It's nice to have a new book on genetic programming. Too bad though that the title really is not very accurate IMO. The book does not really give foundations of GP. It does have theory but the theory does not seem practical. Since this is half the book, I find it disappointing. The second half of the book is a bit better, but again it is not really treating foundations. Instead, it treats very specialised subtopics. It also seems that the book is a collection of papers that the authors have published, so you could get much of this in other places. Since the book is not very original, and the theory is overly complex and not really useful (the book does not say how to use it IMO) I think the book by Banzhaf on GP is much better, even if it is older.
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Most Helpful Customer Reviews on Amazon.com (beta) Amazon.com:
4.8 out of 5 stars (6 customer reviews) 21 of 22 people found the following review helpful:
5.0 out of 5 stars
Exciting New Developments in EC Theory,
By Chris Stephens - Published on Amazon.com
This review is from: Foundations of Genetic Programming (Hardcover)
Langdon and Poli are both internationally recognized experts in Evolutionary Computation (EC) and, in particular, Genetic Programming. They have both contributed extensively to the theoretical "foundations" of GP and hence may speak with no small degree of authority about GP theory. As a physicist working in EC I like the balance that the authors have struck between mathematical rigor and understandable intuition. The book is not as rigorous as Vose's well known GA book. However, it is much easier to read. Neither does it take the "engineering" rule of thumb approach, as does Goldberg's book for instance. It covers very well recent important developments in the theory of GP and in that sense makes very good reading for anyone with a serious interest in EC theory. It is not for the novice, even though technically it is not a difficult book. It is really a research monograph and not a textbook. In that sense the title is a little bit misplaced. With the exciting direction the authors are pointing in I believe that in five years time another book of the same title should truly be able to lay out what are the foundations of GP theory and also show the theoretical unity that exists between the different branches of EC.
18 of 19 people found the following review helpful:
5.0 out of 5 stars
A survey of what was new in 2002,
By dean_from_sa - Published on Amazon.com
This review is from: Foundations of Genetic Programming (Hardcover)
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. 15 of 16 people found the following review helpful:
5.0 out of 5 stars
Good introduction to GP theory,
By A Customer - Published on Amazon.com
This review is from: Foundations of Genetic Programming (Hardcover)
Langdon and Poli do a fantastic job of summarizing the major theoretical results of genetic programming. The first chapter gives a quick and clear introduction to genetic programming. They continue with a comprehensive summary of previous research in schema theory, and then they present their exciting theoretical results. Their description of an exact schema theorem (microscopic and macroscopic) for GP is a bit dense, but they provide a good discussion of how to interpret these results. As a whole, this book is generally easy to follow, even with little prior exposure to genetic programming. Of course, this book is not intended to be a general introduction to genetic programming (one of John Koza's books would be more appropriate), but instead it is intended to present some of the theoretical foundations of the field.
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