Meta-Heuristics

1. Front Matter

   Pages i-x

   PDF

2. Meta-Heuristics: An Overview

   • Ibrahim H. Osman, James P. Kelly

   Pages 1-21

3. A Parallel Genetic Algorithm for the Set Partitioning Problem

   • David Levine

   Pages 23-35

4. Evolutionary Computation and Heuristics

   • Zbigniew Michalewicz

   Pages 37-52

5. Gene Pool Recombination in Genetic Algorithms

   • Heinz Mühlenbein, Hans-Michael Voigt

   Pages 53-62

6. Genetic and Local Search Algorithms as Robust and Simple Optimization Tools

   • Mutsunori Yagiura, Toshihide Ibaraki

   Pages 63-82

7. Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree

   • Geoff Craig, Mohan Krishnamoorthy, M. Palaniswami

   Pages 83-96

8. An Aggressive Search Procedure for the Bipartite Drawing Problem

   • Rafael Martí

   Pages 97-113

9. Guided Search for the Shortest Path on Transportation Networks

   • Yazid M. Sharaiha, Richard Thaiss

   Pages 115-131

10. A Metaheuristic for the Timetabling Problem.

    • H. Abada, E. El-Darzi

    Pages 133-149

11. Complex Sequencing Problems and Local Search Heuristics

    • Peter Brucker, Johann Hurink

    Pages 151-166

12. Heuristic Algorithms for Single Processor Scheduling with Earliness and Flow Time Penalties

    • Mauro Dell’Amico, Silvano Martello, Daniele Vigo

    Pages 167-182

13. Heuristics for the Optimal Control of Thermal Energy Storage

    • Gregor P. Henze, Manuel Laguna, Moncef Krarti

    Pages 183-201

14. Exploiting Block Structure to Improve Resource-Constrained Project Schedules

    • Helmut E. Mausser, Stephen R. Lawrence

    Pages 203-217

15. Combining the Large-Step Optimization with Tabu-Search: Application to The Job-Shop Scheduling Problem

    • Helena Ramalhinho Lourenço, Michiel Zwijnenburg

    Pages 219-236

16. Job-Shop Scheduling by Simulated Annealing Combined with Deterministic Local Search

    • Takeshi Yamada, Ryohei Nakano

    Pages 237-248

17. Cybernetic Optimization by Simulated Annealing: An Implementation of Parallel Processing Using Probabilistic Feedback Control

    • Mark A. Fleischer, Sheldon H. Jacobson

    Pages 249-264

18. A simulated annealing algorithm for the computation of marginal costs of telecommunication links

    • J. L. Lutton, E. Philippart

    Pages 265-275

19. Learning to Recognize (Un)Promising Simulated Annealing Runs: Efficient Search Procedures for Job Shop Scheduling and Vehicle Routing

    • Norman M. Sadeh, Yoichiro Nakakuki, Sam R. Thangiah

    Pages 277-297

20. A Preliminary Investigation into the Performance of Heuristic Search Methods Applied to Compound Combinatorial Problems

    • Mike B. Wright, Richard C. Marett

    Pages 299-317

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

• Institute of Mathematics and Statistics, University of Kent, Cantebury Kent, UK

  Ibrahim H. Osman

• School of Business, University of Colorado, Boulder, USA

  James P. Kelly

Policies and ethics