Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as meta heuristics. Examples of meta heuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a meta heuristic with other optimization techniques, a so called hybrid meta heuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid meta heuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid meta heuristics to novices of the field, and to provide researchers from the field with a collection of some of the meta heuristic interesting recent developments.