This is a compendium of 40 research papers, with an overall length of more than 1,000 pages. The collected papers report on advances in the field of evolutionary computing (EC), a branch of computing founded on the principles of natural evolution. The fundamental characteristic of evolutionary algorithms (EAs) is that they mirror processes that occur during the course of evolution in nature. EC may be visualized as an amalgam, comprising genetic algorithms, genetic programming, evolution strategies, and evolutionary programming.

An EA may be thought of as a repetitive stochastic process that works on a group of entities. A stochastic process is a statistical process that involves several random variables that are contingent on a variable parameter, such as time. The group of entities is often referred to as a population. Every entity typifies or symbolizes a possible solution for the problem at hand.

An EA incorporates three phases: selection, reproduction, and replacement. In the selection phase, the most suitable entities are chosen. In the reproduction phase, existing entities produce offspring, namely, new entities. In the replacement phase, some of the older entities are replaced by newer ones. All three phases are repeated cyclically until a stopping condition is fulfilled. If this happens, a satisfactory solution for the given problem may be found. By nature, EAs are basically heuristics, and hence do not promise any ideal or optimum solution.

The book itself is made up of two parts, one emphasizing the theory of EC, and the other focusing on the application of EC to practical solutions for real-life problems. The theory part of the book includes topics such as frameworks for studying evolution; fast EAs; visualizing EAs; new schemes of biologically inspired EC; the design of EAs that are specific for a problem, as opposed to EAs that are designed without taking into consideration the problem being solved; the involvement of several parents in producing offspring (multi-parent recombination); EAs for dynamic optimization problems; EAs with multiple objectives; scalable genetic search; the solution of permutation problems; a probabilistic evolutionary framework; computational embryology; evolutionary approaches to synthetic biology; scatter search; ant colony optimization; and evolving coordinated agents.

The practice part of the book discusses applications of EC for real-world problems. It includes topics such as the merger of local search and evolutionary search methods for training neural networks; using EC for the design of analog and very large scale integration (VLSI) circuits; the placement of antennas by means of an EA; using EAs for routing optimization in corporate networks, timetabling, job shop scheduling, and scheduling the services of bus drivers; using EAs for data mining and knowledge discovery; the application of EC for data mining from clinical data, image segmentation, and chemical spectrum recognition; the application of EC for protein folding, and for determining the seizing motion of a robot hand; and the design of a program for playing checkers at a level on par with human experts. Although all of the papers in the book were very interesting, twoon scheduling the services of bus drivers, and on creating a program for playing checkerswere notable.

This encyclopedia of EC is part of a series on natural computing. It was written by experts in the field, and is intended for readers who wish to understand the latest developments in this area. Technically, the book is very sophisticated; readers must possess a sound understanding of the basics of EC in order to comprehend the subject matter. Every paper in the book provides adequate pointers to the literature, however, so novice readers should have little difficulty using this book as a resource. The book will therefore be useful both for researchers in the field, and possibly as course material in advanced graduate courses. It will also be a useful reference for practitioners who wish to implement the concepts of EC. It should definitely be of great help to students pursuing research projects or searching for a thesis topic.

The book is well organized, and the topics are covered in a cohesive manner. The depth and coverage of both theory and practice is very impressive. There are many tables, illustrations, and algorithms included, along with a handy index. On the whole, this book will be a valuable reference on evolutionary computing.