Handbook of Metaheuristics

1. Front Matter

   Pages i-xix

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2. Simulated Annealing

   • Alexander G. Nikolaev, Sheldon H. Jacobson

   Pages 1-39

3. Tabu Search

   • Michel Gendreau, Jean-Yves Potvin

   Pages 41-59

4. Variable Neighborhood Search

   • Pierre Hansen, Nenad Mladenović, Jack Brimberg, José A. Moreno Pérez

   Pages 61-86

5. Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications

   • Mauricio G.C. Resende, Celso C. Ribeiro, Fred Glover, Rafael Martí

   Pages 87-107

6. Genetic Algorithms

   • Colin R. Reeves

   Pages 109-139

7. A Modern Introduction to Memetic Algorithms

   • Pablo Moscato, Carlos Cotta

   Pages 141-183

8. Genetic Programming

   • William B. Langdon, Robert I. McKay, Lee Spector

   Pages 185-225

9. Ant Colony Optimization: Overview and Recent Advances

   • Marco Dorigo, Thomas Stützle

   Pages 227-263

10. Advanced Multi-start Methods

    • R. Martí, J. Marcos Moreno-Vega, A. Duarte

    Pages 265-281

11. Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications

    • Mauricio G.C. Resende, Celso C. Ribeiro

    Pages 283-319

12. Guided Local Search

    • Christos Voudouris, Edward P.K. Tsang, Abdullah Alsheddy

    Pages 321-361

13. Iterated Local Search: Framework and Applications

    • Helena R. Lourenço, Olivier C. Martin, Thomas Stützle

    Pages 363-397

14. Large Neighborhood Search

    • David Pisinger, Stefan Ropke

    Pages 399-419

15. Artificial Immune Systems

    • Julie Greensmith, Amanda Whitbrook, Uwe Aickelin

    Pages 421-448

16. A Classification of Hyper-heuristic Approaches

    • Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, John R. Woodward

    Pages 449-468

17. Metaheuristic Hybrids

    • Günther R. Raidl, Jakob Puchinger, Christian Blum

    Pages 469-496

18. Parallel Meta-heuristics

    • Teodor Gabriel Crainic, Michel Toulouse

    Pages 497-541

19. Reactive Search Optimization: Learning While Optimizing

    • Roberto Battiti, Mauro Brunato

    Pages 543-571

20. Stochastic Search in Metaheuristics

    • Walter J. Gutjahr

    Pages 573-597

The rst edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous - velopments observed in the eld of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. For different reasons, Fred and Gary were unable to accept Springer’s invitation to prepare this second e- tion and they suggested that we should take over the editorship responsibility of the Handbook. We are deeply honored and grateful for their trust. As stated in the rst edition, metaheuristics are “solution methods that orch- trate an interaction between local improvement procedures and higher level stra- gies to create a process capable of escaping from local optima and performing a robust search of a solution space. ” Although this broad characterization still holds today, many new and exciting developments and extensions have been observed in the last few years. We think in particular to hybrids, which take advantage of the strengths of each of their individual metaheuristic components to better explore the solution space. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to nd high-quality solutions to an ever-growing number of complex, ill-de ned re- world problems, in particular combinatorial ones.

• algorithms
• genetic algorithm
• genetic algorithms
• genetic programming
• heuristics
• learning
• metaheuristic
• operations research
• optimization

• , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, Canada

  Michel Gendreau

• Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Montreal, Canada

  Jean-Yves Potvin

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