There are several challenges when dealing with optimization problems. For instance, a systematic way to explore the domain of interest must be devised. In many optimization problems, the path to the global optimum is highly nonlinear. That is, the direction of exploration may not be determined according to the information at hand. Another common issue relates to the decision on continuous search (given that a relatively good solution, or a local optimum, is found).

This book provides a rich collection of stochastic optimization algorithms and heuristics that cope with optimization issues. There are a total of 24 chapters. Each chapter covers a specific algorithm. They do not seem to follow any particular order and are independent of each other. Ruin and recreate, simulated annealing, neural networks, genetic algorithms, and ant colony optimization are among the techniques covered in the book.

Choosing the best stochastic optimization technique for a problem often requires practice and experience. Careful planning is necessary to put the constraints and objective of the problem into the framework of the algorithm. In the second part of the book, the traveling salesman problem and the constraint satisfaction problem are used to fill the gap between theories and applications. These chapters enhance the learning experience. Finally, a compact chapter presents the future of optimization algorithms and problems in the third part.

Stochastic optimization is an important technique since many research problems involve it in one way or another. The authors have done an excellent job of putting a great variety of algorithms together. This is an invaluable reference for not only computer science, but for other related disciplines as well. Given the limited space of each chapter, only the most important aspects, as determined by the authors, are described. Therefore, the reader should consult a more specialized source for in-depth study of any particular scheme covered in the book. Another issue is that not many examples or exercises are provided to enhance the learning experience. If used as a textbook, supporting material will be critical for students who are being exposed to this subject for the first time.

In summary, this is a good book on stochastic optimization. It is an important book for any engineering library or laboratory. In my opinion, this book may be used as a quick reference for sophisticated scholars, or as an introductory book for students who are interested in an overview of the state-of-the-art mechanisms in this field.