Metaheuristics

1. Front Matter

   Pages i-xv

   PDF

2. A Path Relinking Algorithm for the Generalized Assignment Problem

   • Laurent Alfandari, Agnès Plateau, Pierre Tolla

   Pages 1-17

3. The PROBE Metaheuristic and Its Application to the Multiconstraint Knapsack Problem

   • Mousbah Barake, Pierre Chardaire, Geoff P. McKeown

   Pages 19-36

4. Lagrangian Heuristics for the Linear Ordering Problem

   • Alexandre Belloni, Abilio Lucena

   Pages 37-63

5. Enhancing the Performance of Memetic Algorithms by Using a Matching-Based Recombination Algorithm

   • Regina Berretta, Carlos Cotta, Pablo Moscato

   Pages 65-90

6. Multi-Cast Ant Colony System for the Bus Routing Problem

   • Urszula Boryczka, Mariusz Boryczka

   Pages 91-125

7. Study of Genetic Algorithms with Crossover Based on Confidence Intervals as an Alternative to Classical Least Squares Estimation Methods for Nonlinear Models

   • Domingo Ortiz-Boyer, César Harvás-Martínez, José Muñoz-Pérez

   Pages 127-151

8. Variable Neighborhood Search for Nurse Rostering Problems

   • Edmund Burke, Patrick De Causmaecker, Sanja Petrovic, Greet Vanden Berghe

   Pages 153-172

9. A Potts Neural Network Heuristic for the Class/Teacher Timetabling Problem

   • Marco P. Carrasco, Margarida V. Pato

   Pages 173-186

10. Genetic Algorithms for the Single Source Capacitated Location Problem

    • Maria João Cortinhal, Maria Eugénia Captivo

    Pages 187-216

11. An Elitist Genetic Algorithm for Multiobjective Optimization

    • Lino Costa, Pedro Oliveira

    Pages 217-236

12. HSF: The iOpt’s Framework to Easily Design Metaheuristic Methods

    • Raphaël Dorne, Christos Voudouris

    Pages 237-256

13. A Distance-Based Selection of Parents in Genetic Algorithms

    • Zvi Drezner, George A. Marcoulides

    Pages 257-278

14. Experimental Pool Design: Input, Output and Combination Strategies for Scatter Search

    • Peter Greistorfer

    Pages 279-300

15. Evolutionary Proxy Tuning for Expensive Evaluation Functions: A Real-Case Application to Petroleum Reservoir Optimization

    • Baris Güyagüler, Roland N. Horne

    Pages 301-324

16. An Analysis of Solution Properties of the Graph Coloring Problem

    • Jean-Philippe Hamiez, Jin-Kao Hao

    Pages 325-345

17. Developing Classification Techniques from Biological Databases Using Simulated Annealing

    • B. de la Iglesia, J. J. Wesselink, V. J. Rayward-Smith, J. Dicks, I. N. Roberts, V. Robert et al.

    Pages 347-367

18. A New Look at Solving Minimax Problems with Coevolutionary Genetic Algorithms

    • Mikkel T. Jensen

    Pages 369-384

19. A Performance Analysis of Tabu Search for Discrete-Continuous Scheduling Problems

    • Joanna Józefowska, Grzegorz Waligóra, Jan Węglarz

    Pages 385-404

20. Elements for the Description of Fitness Landscapes Associated with Local Operators for Layered Drawings of Directed Graphs

    • Pascale Kuntz, Bruno Pinaud, Rémi Lehn

    Pages 405-420

Combinatorial optimization is the process of finding the best, or optimal, so­ lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo­ cation, logic, and assignment of resources. The economic impact of combi­ natorial optimization is profound, affecting sectors as diverse as transporta­ tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu­ nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so­ lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op­ timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu­ tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids.

• Computer
• Operations Research
• Operator
• algorithm
• algorithms
• classification
• communication
• computer science
• database
• genetic algorithms
• heuristics
• learning
• optimization
• programming

• AT&T Labs — Research, USA

  Mauricio G. C. Resende

• INESC, Porto, Portugal

  Jorge Pinho Sousa

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