The Design of Innovation (Genetic Algorithms and Evolutionary Computation) [Hardcover]

David Goldberg (Author)

Book Description

Series: Genetic Algorithms and Evolutionary Computation | Publication Date: June 30, 2002

The Design of Innovation illustrates how to design and implement competent genetic algorithms-genetic algorithms that solve hard problems quickly, reliably, and accurately-and how the invention of competent genetic algorithms amounts to the creation of an effective computational theory of human innovation. For the specialist in genetic algorithms and evolutionary computation, this book combines over two decades of hard-won research results in a single volume to provide a comprehensive step-by-step guide to designing genetic algorithms that scale well with problem size and difficulty. For the innovation researcher - whether from the social and behavioral sciences, the natural sciences, the humanities, or the arts - this unique book gives a consistent and valuable mathematical and computational viewpoint for understanding certain aspects of human innovation. For all readers, The Design of Innovation provides an entrée into the world of competent genetic algorithms and innovation through a methodology of invention borrowed from the Wright brothers. Combining careful decomposition, cost-effective, little analytical models, and careful design, the road to competence is paved with easily understood examples, simulations, and results from the literature.

Editorial Reviews

Review

advance praise:
"It is well known that "building blocks", whether they be the atoms of chemistry, the words of a language, or the modules of a computer, play a key role in our understanding of the world. However, it is hard to find an in-depth discussion of why this is so. It is even more difficult to find a guidebook for using building blocks to make the discoveries that extend science and engineering into new realms. David Goldberg uses his extensive experience with Genetic Algorithms to provide a superb guidebook for exploiting building blocks, combining relevant theory with carefully chosen examples. If you are a scientist or an engineer concerned with innovation, you should give this unique book a close reading."
(John H. Holland, University of Michigan)
"Dave Goldberg's first book, Genetic Algorithms in Search, Optimization, and Machine Learning, gave the field of genetic and evolutionary computation widespread attention among practicing engineers and researchers of machine learning and artificial intelligence. His latest effort, The Design of Innovation, is likely to transform the practice of all forms of genetic and evolutionary computation. For much of the last decade, theoreticians and practitioners have worked independently of one another. In this masterstroke of a book, Goldberg de-Balkanizes the field and bridges the chasm between theory and practice with his "little models," dimensional analysis, and "patchquilt integration." Not only does he show a clear path toward the principled design of scalable genetic and evolutionary computation, he suggests how these computations lead to a computational theory of the innovative. Much of what is presented is likely to be controversial, but whether you agree with him or not, Goldberg's arguments are first rate, and this book is a terrific read. I urge those interested in innovation in general or genetic and evolutionary computation in particular to buy this book and study it closely."
(John R. Koza, Stanford University)
"David Goldberg's treatise, The Design of Innovation, is unlike any other book in the vast literature on genetic algorithms and evolutionary computation. Its ambitious aim is to develop a coherent theory of design and innovation in the context of what the author calls competent GAs, that is, GAs that work well. But an even more ambitious aim is to use competent GAs as a platform for construction of computational models of innovation and creativity-concepts which are notoriously hard to formalize. One cannot but be greatly impressed by the many novel ideas which are presented in The Design of Innovation in a lively, insightful and reader-friendly style. The Design of Innovation is an original work which is a must reading for anyone who is interested in genetic algorithms, evolutionary computation and, more generally, in design and innovation. David Goldberg deserves our thanks and congratulations."
(Lotfi A. Zadeh, University of California, Berkeley)

Product Details

• Hardcover: 272 pages
• Publisher: Springer; 1 edition (June 30, 2002)
• Language: English
• ISBN-10: 1402070985
• ISBN-13: 978-1402070983
• Product Dimensions: 9.6 x 6.2 x 0.8 inches
• Shipping Weight: 1.3 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #2,127,375 in Books (See Top 100 in Books)
  • #46 in Books > Computers & Technology > Programming > Algorithms > Genetic

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Customer Reviews

Most Helpful Customer Reviews

44 of 49 people found the following review helpful:

4.0 out of 5 stars Great for GA-centered research, doubtful otherwise, December 13, 2002

By A Customer

This review is from: The Design of Innovation (Genetic Algorithms and Evolutionary Computation) (Hardcover)

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.

In sum, this book is a well-written research monograph intended to open up further research into the heart and soul of GAs. It should be read by researchers in AI, machine learning, and related fields. However, it will not provide the immediate answers to practitioners who are now running into the limitations of GAs (and other evolutionary or general search techniques).