This volume is a collection of papers on various topics, most of them related to algorithms for various problems. The editors have attempted to produce a volume that bridges the gap between algorithmic theory and applications by providing researchers interested in applications with surveys on algorithms for various problems.
The breadth of topics is immense, ranging from the generation of families of combinatorial objects through algorithms for reaction-diffusion problems to data mining and network routing. Some of the topics are well established, while others are relatively new. However, some topics receive a great deal of attention while other important topics receive no coverage at all. One could argue that the volume should have included surveys on topics such as algorithms for signal processing, linear programming, and numerical linear algebra.
While some of the papers are broad surveys on particular algorithmic topics, other papers are very narrowly focused. For example, the chapter on "Applying Evolutionary Algorithms to Solve the Automatic Frequency Planning Problem" gives a nice example of the use of evolutionary algorithms to solve a particular problem but certainly isn't a broad survey on evolutionary algorithms. Some of the papers contain little or no discussion of algorithmic issues. For example, a chapter on "Graph Theoretic Models in Chemistry and Molecular Biology" discusses a number of mathematical models in chemistry and molecular biology that can be solved by computer but doesn't discuss any of the algorithmic details of the solution of these models.
Unfortunately, this book fails miserably as a comprehensive reference handbook on applied algorithms. Given the unfocused nature of this collection it's hard to imagine that anyone would want to read the book from cover to cover. At best, some readers might find a chapter or two that is of interest.
Brian Borchers is a professor of Mathematics at the New Mexico Institute of Mining and Technology. His interests are in optimization and applications of optimization in parameter estimation and inverse problems.