Preface.
1. Tight relaxations for nonconvex optimization problems using the Reformulation-Linearization/Convexification Technique (RLT); H.D. Sherali.
2. Exact algorithms for global optimization of mixed-integer nonlinear programs; M. Tawarmalani, N.V. Sahinidis.
3. Algorithms for global optimization and discrete problems based on methods for local optimization; W. Murray, Kien-Ming Ng.
4. An introduction to dynamical search; L. Pronzato, et al.
5. Two-phase methods for global optimization; F. Schoen.
6. Simulated annealing algorithms for continuous global optimization; M. Locatelli.
7. Stochastic Adaptive Search; G.R. Wood, Z.B. Zabinsky.
8. Implementation of Stochastic Adaptive Search with Hit-and-Run as a generator; Z.B. Zabinsky, G.R. Wood.
9. Genetic algorithms; J.E. Smith.
10. Dataflow learning in coupled lattices: an application to artificial neural networks; J.C. Principe, et al.
11. Taboo Search: an approach to the multiple-minima problem for continuous functions; D. Cvijovic, J. Klinowski.
12. Recent advances in the direct methods of X-ray crystallography; H.A. Hauptman.
13. Deformation methods of global optimization in chemistry and physics; L. Piela.
14. Experimental analysis of algorithms; C.C. McGeoch.
15. Global optimization: software, test problems, and applications; J.D. Pintér.

In 1995 the Handbook of Global Optimization (first volume), edited by R. Horst, and P.M. Pardalos, was published. This second volume of the Handbook of Global Optimization is comprised of chapters dealing with modern approaches to global optimization, including different types of heuristics.

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, algorithms based on local optimization, and dynamical search. Finally, the book contains chapters on experimental analysis of algorithms and software, test problems, and applications.