Simulated Annealing -- Tabu Search -- Variable Neighborhood Search -- Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications -- Genetic Algorithms -- A Modern Introduction to Memetic Algorithms -- Genetic Programming -- Ant Colony Optimization: Overview and Recent Advances -- Advanced Multi-start Methods -- Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications -- Guided Local Search -- Iterated Local Search: Framework and Applications -- Large Neighborhood Search -- Artificial Immune Systems -- A Classification of Hyper-heuristic Approaches -- Metaheuristic Hybrids -- Parallel Meta-heuristics -- Reactive Search Optimization: Learning While Optimizing -- Stochastic Search in Metaheuristics -- An Introduction to Fitness Landscape Analysis and Cost Models for Local Search -- Comparison of Metaheuristics.

“… an excellent book if you want to learn about a number of individual metaheuristics." (U. Aickelin, Journal of the Operational Research Society, Issue 56, 2005, on the First Edition) The first edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous developments observed in the field of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. When Glover and Kochenberger were unable to prepare this second edition, they suggested that Michel Gendreau and Jean-Yves Potvin should take over the editorship, and so this important new edition is now available. Through its 21 chapters, this second edition is designed to provide a broad coverage of the concepts, implementations and applications in this important field of optimization. Original contributors either revised or updated their work, or provided entirely new chapters. The Handbook now includes updated chapters on the best known metaheuristics, including simulated annealing, tabu search, variable neighborhood search, scatter search and path relinking, genetic algorithms, memetic algorithms, genetic programming, ant colony optimization, multi-start methods, greedy randomized adaptive search procedure, guided local search, hyper-heuristics and parallel metaheuristics. It also contains three new chapters on large neighborhood search, artificial immune systems and hybrid metaheuristics. The last four chapters are devoted to more general issues related to the field of metaheuristics, namely reactive search, stochastic search, fitness landscape analysis and performance comparison.