Theory and Applications

  • Ibrahim H. Osman
  • James P. Kelly

Table of contents

  1. Front Matter
    Pages i-x
  2. Ibrahim H. Osman, James P. Kelly
    Pages 1-21
  3. Zbigniew Michalewicz
    Pages 37-52
  4. Heinz Mühlenbein, Hans-Michael Voigt
    Pages 53-62
  5. Mutsunori Yagiura, Toshihide Ibaraki
    Pages 63-82
  6. Geoff Craig, Mohan Krishnamoorthy, M. Palaniswami
    Pages 83-96
  7. Yazid M. Sharaiha, Richard Thaiss
    Pages 115-131
  8. H. Abada, E. El-Darzi
    Pages 133-149
  9. Peter Brucker, Johann Hurink
    Pages 151-166
  10. Mauro Dell’Amico, Silvano Martello, Daniele Vigo
    Pages 167-182
  11. Gregor P. Henze, Manuel Laguna, Moncef Krarti
    Pages 183-201
  12. Helmut E. Mausser, Stephen R. Lawrence
    Pages 203-217
  13. Ahmed S. Al-Mahmeed
    Pages 319-330
  14. Roberto Battiti, Giampietro Tecchiolli, Paolo Tonella
    Pages 331-342
  15. Diane Castelino, Nelson Stephens
    Pages 343-359
  16. Mauro Dell’Amico, Francesco Maffioli
    Pages 361-377
  17. Fred Glover, Gary A. Kochenberger
    Pages 407-427
  18. S. Hanafi, A. Freville, A. El Abdellaoui
    Pages 449-465
  19. Lutz Sondergeld, Stefan Voß
    Pages 489-502
  20. Michel Toulouse, Teodor G. Crainic, Michel Gendreau
    Pages 503-522
  21. Vicente Valls, M. Ángeles Pérez, M. Sacramento Quintanilla
    Pages 537-553
  22. David L. Woodruff
    Pages 555-569
  23. Martin Zachariasen, Martin Dam
    Pages 571-587
  24. Irène Charon, Olivier Hudry
    Pages 589-603
  25. H. M. M. ten Eikelder, M. G. A. Verhoeven, T. W. M. Vossen, E. H. L. Aarts
    Pages 605-618
  26. Jean-Yves Potvin, François Guertin
    Pages 619-631

About this book


Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications.
This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.


Optimization Theory algorithm algorithms artificial intelligence combinatorial optimization communication genetic algorithms heuristics learning metaheuristic operations research optimization programming scheduling

Editors and affiliations

  • Ibrahim H. Osman
    • 1
  • James P. Kelly
    • 2
  1. 1.Institute of Mathematics and StatisticsUniversity of KentCantebury KentUK
  2. 2.School of BusinessUniversity of ColoradoBoulderUSA

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