The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.

The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.

Problems to Be Solved.- Evolutionary Computing: The Origins.- What Is an Evolutionary Algorithm?.- Representation, Mutation, and Recombination.- Fitness, Selection, and Population Management.- Popular Evolutionary Algorithm Variants.- Hybridisation with Other Techniques: Memetic Algorithms.- Nonstationary and Noisy Function Optimisation.- Multiobjective Evolutionary Algorithms.- Constraint Handling.- Interactive Evolutionary Algorithms.- Coevolutionary Systems.- Theory.- Evolutionary Robotics.- Parameters and Parameter Tuning.- Parameter Control.- Working with Evolutionary Algorithms.- References.