The Design of Innovation: Lessons from and for Competent Genetic Algorithms

The Design of Innovation: Lessons from and for Competent Genetic Algorithms

by David Goldberg, Goldberg
     
 

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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

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Overview

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.

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Editorial Reviews

Goldberg (U. of Illinois at Urbana-Champaign) brings together a genetic algorithms design methodology, applicable pieces of design theory, and genetic algorithms designs. He develops models that give explicit guidance on how to design genetic algorithms that work, what he calls "competent GAs." A basic understanding of calculus, probability, and statistics is suggested. Annotation c. Book News, Inc., Portland, OR

Product Details

ISBN-13:
9781402070983
Publisher:
Springer-Verlag New York, LLC
Publication date:
06/28/2002
Series:
Genetic Algorithms and Evolutionary Computation Series, #7
Edition description:
New Edition
Pages:
272
Product dimensions:
6.30(w) x 9.50(h) x 0.80(d)

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