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This is a very good introductory text for those starting out in non-traditional artificial intelligence or soft computing. It gives a survey of the three major branches within the field along with examples. While it is not explicitly written to be a text book for teaching a computational intelligence course, there are recommendations in the first chapter and sample problems throughout that would make this transition fairly straightforward (but no answers are given for these problems). The broad approach makes it a good choice for an instructor seeking a text for an introductory course or set of courses. The book is organized in three sections, each matching one of the three major branches of computational intelligence with four chapters dedicated to each. The material is most appropriate for a student in science, technology, engineering or mathematics who is completing their final year of study for a bachelor’s degree or is at an early post graduate level. As would be expected, a fairly strong background in mathematics is recommended before approaching any of the topics covered in the book. Readers will want to be familiar with integral calculus, linear algebra, and set theory. The first section covers Neural Networks. The foundation of the area is well laid and many of the major approaches are discussed, and I was happy to see that Convolutional Neural Networks were also included. The progression through the chapters builds up in a logical manner from basic Artificial Neural Networks to a discussion on Hopfield Networks and Grossberg Networks. I would have like to have seen some discussion on Support Vector Machines, but this may have been an omission to limit the length of the text. The material discussing radial basis

James Keller, Derong Liu, and David Fogel: Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation