PDF Download Foundations of Genetic Programming
PDF Download Foundations of Genetic Programming
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Foundations of Genetic Programming
PDF Download Foundations of Genetic Programming
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Review
From the reviews: I came to this book from an engineering perspective as a GP practitionerinterested in practical issues such as which cross-over operator was mostapplicable for my problem. Whilst this book did not offer any clear-cutanswers, 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 overviewof the body of work which will, in time, come to provide a theoreticalfoundation, and accurate prescriptions, for all of the ad-hoc tweaks andadjustments 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." (VÃt Fábera, Neural Network World, Vol. 12 (4), 2002)
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Product details
Hardcover: 260 pages
Publisher: Springer; 2002 edition (March 22, 2002)
Language: English
ISBN-10: 3540424512
ISBN-13: 978-3540424512
Product Dimensions:
9.2 x 0.7 x 6.1 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#641,614 in Books (See Top 100 in Books)
A truly classic text in the field of evolutionary computation. As an artificial intelligence researcher, I consider this an essential text to have on my bookshelf. It is one of the most cited texts in the field, and Langdon is considered one of the leading minds in the evolutionary programming and genetic algorithms field.
This book can be usefully read along with a companion text by the same publisher - "Introduction to Evolutionary Computing". Langdon and Poli provide a focused look, on the specifics of genetic programming. The maths treatment here is significantly more involved than the other book.Foundations starts with what I suppose in this field is an obligatory section on the concept of a fitness landscape. A very useful metaphor of what you'll be attempting to do, as a researcher. However, the authors carefully point out the limitations of this idea. Notably that some spaces might have no natural metric.The book then rapidly goes into the ideas of GP schemas and hyperschemas. Accompanied by a nice theoretical analysis of key performance goals like the rate of convergence in the GP search space. A solid offering to the GP researcher.
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.
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.
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.
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