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Genetic algorithms and grouping problems by Emanuel Falkenauer

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Published by Wiley in Chichester, New York .
Written in

Subjects:

  • Genetic algorithms.

Book details:

Edition Notes

Includes bibliographical references (p. [213]-217) and index.

StatementEmanuel Falkenauer.
Classifications
LC ClassificationsQA402.5 .F25 1998
The Physical Object
Paginationxvi, 220 p. :
Number of Pages220
ID Numbers
Open LibraryOL684702M
ISBN 100471971502
LC Control Number97031527

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Oct 30,  · Emanuel Falkenauer is the author of Genetic Algorithms and Grouping Problems, published by judybwolfman.com: Emanuel Falkenauer. Emanuel Falkenauer is the author of Genetic Algorithms and Grouping Problems, published by judybwolfman.com: $ This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Genetic algorithms and grouping problems [Book Review] Article (PDF Available) in IEEE Transactions on Evolutionary Computation 5(3) - · .

A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. The book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Cited by: From the Publisher: A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. The book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Falkenauer's new class of algorithms. In this paper we present the Grouping Genetic Algorithm (GGA), which is a Genetic Algorithm (GA) heavily modified to suit the structure of grouping problems. We first show why both the standard and the ordering GAs fare poorly in this domain, by pointing out their inherent difficulty to capture the regularities of the functional landscape of the grouping judybwolfman.com by: 5. Apr 22,  · Description A reader-friendly introduction to the exciting, vast potential of Genetic Algorithms. The book gives readers a general understanding of the concepts underlying the technology, an insight into its perceived benefits and failings, and a clear and practical illustration of how optimization problems can be solved more efficiently using Falkenauer's new class of judybwolfman.com: Emanuel Falkenauer.

Genetic Algorithms and Grouping Problems is written for those designing or creating real-world genetic algorithms, whether practitioner or researcher. In addition, the very nature of this author's discourse will inspire anyone wishing to understand both the benefits and the limitations of GAs.4/5(4). This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision judybwolfman.com: Michael Mutingi. The Grouping Genetic Algorithm. 3. Grouping Problems. 4. Drawbacks of Previous GAs for Grouping Problems. 5. The Grouping Genetic Algorithm --pt. 3. Industrial Applications of Grouping Genetic Algorithms. 6. Introduction. 7. Bin Packing and Line Balancing. 8. Economies of Scale. 9. Creation of Part Families --Conceptual Clustering. Equal Piles --pt. 4. Epilogue. Where Have We Been? . Generally speaking, genetic algorithms are simulations of evolution, of what kind ever. In most cases, however, genetic algorithms are nothing else than prob-abilistic optimization methods which are based on the principles of evolution. This idea appears first in in J. D. Bagley’s thesis “The Behavior.