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© 2002

OmeGA

A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems

Book

Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 6)

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Dimitri Knjazew
    Pages 27-49
  3. Dimitri Knjazew
    Pages 51-68
  4. Dimitri Knjazew
    Pages 69-70
  5. Back Matter
    Pages 71-152

About this book

Introduction

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. OmeGA, or the ordering messy genetic algorithm, combines some of the latest in competent GA technology to solve scheduling and other permutation problems. Competent GAs are those designed for principled solutions of hard problems, quickly, reliably, and accurately. Permutation and scheduling problems are difficult combinatorial optimization problems with commercial import across a variety of industries.

This book approaches both subjects systematically and clearly. The first part of the book presents the clearest description of messy GAs written to date along with an innovative adaptation of the method to ordering problems. The second part of the book investigates the algorithm on boundedly difficult test functions, showing principled scale up as problems become harder and longer. Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.

Keywords

Industrie algorithms calculus code combinatorial optimization design development genetic algorithms mutation optimization scheduling technology

Authors and affiliations

  1. 1.SAP AGGermany

Bibliographic information

  • Book Title OmeGA
  • Book Subtitle A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
  • Authors Dimitri Knjazew
  • Series Title Genetic Algorithms and Evolutionary Computation
  • DOI https://doi.org/10.1007/978-1-4615-0807-6
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Hardcover ISBN 978-0-7923-7460-2
  • Softcover ISBN 978-1-4613-5249-5
  • eBook ISBN 978-1-4615-0807-6
  • Series ISSN 1568-2587
  • Edition Number 1
  • Number of Pages XXI, 152
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Artificial Intelligence
    Theory of Computation
    Optimization
  • Buy this book on publisher's site
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