Introduction to Evolutionary Computing

Assembly Automation

ISSN: 0144-5154

Article publication date: 1 September 2004




Eiben, A.E. and Smith, J.E. (2004), "Introduction to Evolutionary Computing", Assembly Automation, Vol. 24 No. 3, pp. 324-324.



Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited

“Introduction to Evolutionary Computing” presents the first complete overview of problem solving techniques based on principles of biological evolution such as genetic inheritance and natural selection.

Chapter 1 is an introduction to the subject and provides the main evolutionary computing metaphor, a brief history, and background coverage of the inspiration from biology. Components of evolutionary algorithms, example applications, and evolutionary computing and global optimisation, are amongst the subjects discussed in Chapter 2, What is an Evolutionary Algorithm? Chapter 3, Genetic Algorithms, discusses subjects including representation of individuals, mutation, recombination, population models, parent and survivor selection, and an example application.

The following two chapters address Evolution Strategies and Evolutionary Programming, respectively. Chapter 6 discusses Genetic programming and covers subjects including initialisation, bloat in genetic programming, and problems involving “physical” environments.

ZCS: a “zeroth‐level” classifier system, XCS, and extensions are amongst the subjects discussed in Chapter 7, Learning Classifier Systems. Chapter 8, Parameter Control in Evolutionary Algorithms, provides examples of changing parameter, classification of control techniques, and examples of varying EA parameters. The following four chapters discuss Multimodal Problems and Spatial Distribution, Hybridisation with Other Techniques: Memetic Algorithms, Theory, and Constraint Handling.

Coevolution, interactive evolution, and non‐stationary function optimisation are discussed in Chapter 13, Special Forms of Evolution, while Chapter 14 presents Working with Evolutionary Algorithms. The final chapter of the book provides a summary and the two appendices address Gray Coding, and Test Functions, respectively.

Overall, this is an extremely well written textbook that draws you into the subject. Each chapter concludes with exercises and a recommended reading list, making it suitable for undergraduate and graduate students, researchers and lecturers from a range of computing, science and engineering disciplines.

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