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The book’s title promises an evolutionary approach to machine learning and deep neural networks. In fact, it covers both an evolutionary approach to machine learning and machine learning approach to evolutionary computations. It presents the current trends in the research area connecting evolutionary algorithms (EAs) and machine learning (ML), and covers a wide range of various hybrid approaches benefiting from a synergy between EAs and ML. The book is organised in six chapters, each chapter beginning with a short abstract and ending with a list of references. The first two chapters are introductory, covering the methods and algorithms (evolutionary computation, machine learning, deep learning) that are used throughout the next three chapters (3–5), which form the core of the book. The last chapter is a short conclusion. The great advantage of the book is the large number of figures, illustrations, and diagrams, making the content easy to understand. The first chapter explains the fundamental principles of evolution and introduces evolutionary computation. The chapter is written in a way that it is understandable for beginners, and more experienced readers may skip it. The list of references at the end of Chapter 1 is far from complete. For example, when introducing genetic programming (GP) I would expect a reference to John Koza. (The corresponding works are cited in the Preface on page v instead.) The second chapter covers selected topics from machine learning and can be skipped by readers with a machine learning background. Chapter 2 is a little bit heterogeneous, individual sections have different presentation styles. Some methods are described in more detail than others. For example, Gröbner bases (a mathematical theory that is needed later in Chapter 4, particularly for the algebraic approach to GP) got much more space than other methods, including deep neural networks (DNN). The reader should not expect to get a complete knowledge of the DNN

Hitoshi Iba: Evolutionary approach to machine learning and deep neural networks: neuro-evolution and gene regulatory networks