The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods.
Éric D. Taillard is a professor at the University of Applied Sciences and Arts of Western Switzerland, HEIG-VD campus in Yverdon-les-Bains. After completing his studies and obtaining a PhD at the Swiss Federal Institute of Technology in Lausanne, he worked as a researcher at the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation in Montreal, Canada, and then at the Dalle Molle Institute for Artificial Intelligence in Lugano, Switzerland.He has over 30 years of research experience in the field of metaheuristics. Outside of Switzerland, he has been invited to teach this subject at various universities: Vienna and Graz in Austria, Nantes in France and Hamburg in Germany.
Innehållsförteckning
Part I: Combinatorial Optimization, Complexity Theory and Problem Modelling.- 1. Elements of Graphs and Complexity Theory.- 2. A Short List of Combinatorial Optimization Problems.- 3. Problem Modelling.- Part II: Basic Heuristic Techniques.- 4. Constructive Methods.- 5. Local Search.- 6. Decomposition Methods.- Part III: Popular Metaheuristics.- 7. Randomized Methods.- 8. Construction Learning.- 9. Local Search Learning.- 10. Population Management.- 11. Heuristics Design.- 12. Codes.