A welcome compendium of papers, this book is edited by two veterans of the computer science field, Stephan Olariu and Albert Y. Zomaya. Both are full professors at their respective universities, and on the editorial boards of a number of important journals, including IEEE Transactions on Parallel and Distributed Systems. This is significant, because the most common “thread” (pardon the pun) binding all of the papers of the book together is bioinspired algorithms for parallel processing systems and applications.

As a researcher who has both published in the field of scheduling for parallel processors, and edited chapters for books on bioinspired algorithms, I am excited about this book. Casual readers who are interested in broadening their horizons in alternative approaches to computing must understand the context of this work. The acceptance of alternative approaches to computing has been slow in the US, as evidenced by the relatively few American universities that offer courses in this field. However, colleagues from laboratories abroad have been known to embrace these concepts, and are more eager to explore their revolutionary paradigms. It is not surprising, therefore, that of the 85 contributors listed in the preface of the book, 68 come from abroad, and 17 are from the US.

The book consists of two sections: models and paradigms, and application domains. The former occupies 11 chapters of the work, while the latter occupies the remaining 25 chapters. This is an appropriate decision by the editors, because the book is not meant to be a review of bioinspired or evolutionary computing; its purpose is rather to notify the reader of the exciting applications that benefit from these technologies. In this context, the first section of the book is intended to present the most recent trends within the field, including evolutionary algorithms (chapter 1), neural networks (chapter 2), ant colony optimization and swarm intelligence (chapters 3 and 4), genetic programming (chapters 5 and 8), cellular algorithms (chapters 6 and 7), and DNA computing (chapter 9).

The second section of the book presents applications that correspond to the technologies of the first section. Scheduling on parallel processing systems is analyzed in three chapters (12, 13, and 17). Various networking problems are explored in five chapters (14, 15, 21, 23, and 33). Ant colony and swarm applications are discussed in four chapters (19 to 22). Six chapters (18, 27, 28, 31, 35, and 36) present various learning paradigms. Neural networks form the core of three chapters (24 to 26). The remaining chapters present a potpourri of topics (engineering, medical, and graphs). Again, all of these chapters benefit from parallel algorithm implementations.

This handbook would be a perfect text for a seminar on bioinspired computing. The dual sections of the book contain the proper proportion of broader surveys to advanced technology. Typically, a course would spend a third of the semester on core concepts, and the remainder on applying the fundamentals to actual problems. Because of the intensive amount of computation necessary to maintain a sufficiently large population of possible solutions, parallel processing systems are employed to speed up the execution of the algorithms. In addition, as computing system trends move toward parallel hardware architectures, a number of associated issues have to be addressed. This book professionally addresses these concerns.