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

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 ...

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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

From The Critics
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
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Product Details

Table of Contents

List of Figures. List of Tables. Preface. Acknowledgments. 1. Genetic Algorithms and Innovation. 2. Making Genetic Algorithms Fly. 3. Three Tools of Conceptual Engineering. 4. Goals and Elements of GA Design. 5. Building Blocks. 6. A Design Approach to Problem Difficulty. 7. Ensuring Building Block Supply. 8. Ensuring Building Block Growth. 9. Making Time for Building Blocks. 10. Deciding Well. 11. Mixing, Control Maps, and GA Success. 12. Design of Competent Genetic Algorithms. Epilogue: from Competence to Efficiency and Beyond. References. Index.

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  • Anonymous

    Posted January 7, 2008

    A reviewer

    David Goldberg's previous book 'Genetic Algorithms for Search, Optimization, and Machine Learning' is very well known and popular introduction to the field of genetic algorithms (GAs). Now in this book, Dr. Goldberg shows how to take GAs to the next level of competence and engineered quality and confidence. He collects results that help estimate the correct sizing of populations for problems of a different sizes. He shows how to break down the problem of acheiving results with GAs into sub-problems that have been addressed by research or are active research topics. I find this book inspiring and have returned to it several times for help in thinking about engineering problems.

    1 out of 1 people found this review helpful.

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