Prof.
Gusz Eiben
received his Ph.D. in Computer Science in 1991. He was among the pioneers of evolutionary computing research in Europe, and served in key roles in steering committees, program committees and editorial boards for all the major related events and publications. His main research areas focused on multiparent recombination, constraint satisfaction, and self-calibrating evolutionary algorithms; he is now researching broader aspects of embodied intelligence and evolutionary robotics.

Prof.
James E. Smith
received his Ph.D. in Computer Science in 1998. He is an associate professor of Interactive Artificial Intelligence and Head of the Artificial Intelligence Research Group in the Dept. of Computer Science and Creative Technologies of The University of the West of England, Bristol. His work has combined theoretical modelling with empirical studies in a number of areas, especially concerning self-adaptive and hybrid systems that "learn how to learn". His current research interests include optimization; machine learning and classification; memetic algorithms; statistical disclosure control; VLSI design verification; adaptive image segmentation and classification and computer vision systems for production quality control; and bioinformatics problems such as protein structure prediction and protein structure comparison.

New edition of well-established undergraduate textbook revised to offer an integrated view on evolution-based problem-solving algorithms

Includes a new chapter on evolutionary robotics

Combines chapters on parameter tuning and control with "how-to" chapters in a new book part dedicated to methodology

Includes supplementary material: [...]

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.