Part I Hybrid metaheuristics for mono and multi-objective optimization, and optimization under uncertainty.- Part II Combining metaheuristics with (complementary) metaheuristics.- Part III Combining metaheuristics with exact methods from mathematical programming approaches.- Part IV Combining metaheuristics with constraint programming approaches.- Part V Combining metaheuristics with machine learning and data mining techniques.

The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.