This text is comprised of chapters dealing with modern approaches to global optimization, including different types of heuristics. The goal of the editors is to provide a true handbook that does not focus on particular applications of the heuristics and algorithms, but rather describes the state of the art for the different methodologies. Topics covered in the handbook include various metaheuristics, such as: simulated annealing; genetic algorithms; neural networks; taboo search; shake-and-bake methods; and deformation methods. In addition, the book contains chapters on new exact stochastic and deterministic approaches to continuous and mixed-integer global optimization, such as: stochastic adaptive search; two-phase methods; branch-and-bound methods with new relaxation and branching strategies; and algorithms based on local optimization, and dynamical search. Finally, the book contains chapters on: the experimental analysis of algorithms and software; test problems; and applications.