We see that javascript is disabled or not supported by your browser - javascript is needed for important actions on the site. Read more

What's New - Home - Login

Member $avings: $81,596,443.65 | Books Available: 785,668 | Members Online: 73

Swap Used Books - Buy New Books at Great Prices!

How To Swap Books Sign Up Search submenu

PBS Market (New Books)

Gift Buying Guide

Book Browser Advanced Search Books Posted Today Member Book Reviews Award Winning Books NYT Best Sellers Amazon Best Sellers Most Traveled Copies Club Wish List

Discussion Forums Book Lists

Club Lists My Book Lists My Watched Lists Create a List

Blog Donations

School Donation Program In Memory of... Military Donation Program Friends of PBS

Box-O-Books Maps The Eclectic Pen Fun Stuff

20 Questions Sudoku Bookmark Creator

Top 100

Requests Posts Swappers Referrers Reviewers

Pulse of PBS Spread The Word

Invite Friends Bookmarks Facebook Page Facebook App More Ways...

Photo Gallery Recipes Club Tag Cloud Member Testimonials

Help Center submenu

How To Swap Books Browse Help Docs Ask the Librarian PBS Member Icons Live Help

Kiosk

PBS Market (New Books) Go Shopping Buy Credits Buy PBS Money Upgrade Membership Gift Certificates Transfer Credits

Need Help?

Search - Genetic Algorithms: The Design of Innovation

Genetic Algorithms The Design of Innovation
Author: David Goldberg, Kumara Sastry

The first edition of this book (Goldberg, 2002) was welcomed as an important contribution to the understanding and design of scalable genetic algorithms. Goldberg's theory of facetwise models proves invaluable to GA understanding and design, and the core chapters of the book continue to make those important arguments; however, they are brought u... more »p to date with the most important recent results, including population timing and sizing results. The chapter on scalable GA design (Chapter 12) gets a thorough overhaul by introducing other key scalable GA techniques, including the DSMGA (Dependency Structure Matrix GA) and others, and discussing how they relate to earlier models. Although the literature tends to emphasize small differences between different methods, the chapter shows the common theoretical and methodological threads running through all scalable methods. The DSMGA results are particularly important because of the light the shed on probabilistic model builders such as the Bayesian Optimization Algorithm. In the first edition, the possibility of efficiency enhancement was discussed briefly, but since 2002, great strides have been made in the practical speedup of scalable genetic algorithms through parallelization, time continuation, problem relaxation, and hybridization. Individually these techniques have demonstrated surefooted effectiveness in speeding GA solutions; however, when used in combination with both structural and fitness model building techniques, genetic algorithms can often be speeded by two or more orders of magnitude in so-called supermultiplicative speedups. This exciting possibility enables the solution of hard problems that were formerly beyond the reach of GAs because solution times and costs were prohibitive. The first edition of the text emphasized the importance of both theory and implementation practice as being important to the solution of real-world problems. A new chapter, Chapter 14, A Billion Variables and Beyond, shows how to put together the ideas in the book toward the solution of problems with millions and billions of decision variables. Traditional operations research and optimization is limited in practice to problems with thousands of decision variables because of the double whammy of the curse of dimensionality and the serial bottlenecks inherent in many of the procedures in common use. This chapter presents recent results in demonstrating practical scalability of GAs on a problem with over a billion variables, and shows how these results can be used to obtain routine solutions on many important problems with millions and even billions of variables. Much of the book is devoted to understanding and applying useful, cool technology on increasingly difficult problems of science, technology, and commerce, but a new final chapter returns to the more philosophical tone of the early part of the text. Scalable genetic algorithms are cool technology, but GA practitioners can hardly help but have the way they think about the world permanently altered by the philosophical possibilities of "population thinking." In the narrow realm of technology, populations represent a disembodied set of solutions to some particular problem, but it does not require an act of great imagination to think of GA populations as groups of agents or organisms or firms or even people. In this way, the lessons learned from this book can be applied to philosophical reflection about a variety of innovative, inventive, or even creative systems. These ideas lead inexorably to wonder about whether computer programs might ever achieve a kind of computational consciousness, and the final chapter concludes with some thoughts on that possibility. The first edition was an important landmark in the theory and practice of genetic algorithms, and problem size and difficulty of problem tackled has progressed rapidly since its publication. Genetic Algorithms: The Design of Innovation (2nd edition) updates that text with important additions, new groundbreaking material, and important suggestions for key research directions and likely lines of successful inquiry.

ISBN-13: 9780387353746
ISBN-10: 0387353747
Publication Date: 4/1/2010
Pages: 336
Edition: 2nd
Rating:

0 stars, based on 0 rating

Publisher: Springer
Book Type: Hardcover
Members Wishing: 0
Reviews: Amazon | Write a Review

Genres:

Computers & Technology >> Computer Science >> AI & Machine Learning >> Machine Learning
Computers & Technology >> Programming >> Algorithms >> Genetic

Back to the Top ↑

Search - Genetic Algorithms: The Design of Innovation

Loading...