Evolutionary algorithms (EAs) are accepted as a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. The unique properties of spatially structured EAs evoke new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Included is new material on non-standard networked population structures such as small-world networks. The book will be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization, and also to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.

Uses graph concepts as a unifying theme to express the state of the art in spatially structured evolutionary algorithms Includes new material on non-standard networked population structures such as small-world networks This is the first book devoted to spatial and temporal aspects of genetic algorithms Useful to a broad readership of students and researchers in fields involving the topological structures of populations and their evolution Includes supplementary material: sn.pub/extras

Autorentext

Bastien Chopard is a professor in the Département d'informatique of the Université de Genève, where he directs the Scientific and Parallel Computing Group. His main research activity concerns the study of complex systems, in particular the development of new numerical methods to model and simulate phenomena in natural sciences, economics, social systems, and biomedical applications, the key tools used being cellular automata, the lattice Boltzmann method, and multiagent techniques. Marco Tomassini is an honorary professor at the Faculty of Business and Economics of the University of Lausanne (HEC). After obtaining a PhD degree in Theoretical Chemistry working on computer simulations of condensed matter systems, he switched to Computer Science and complex systems investigations. His main research activities have since focused on parallel computing, cellular automata, evolutionary algorithms, the structure of difficult problem landscapes, complex networks, and evolutionary games.

Inhalt
Setting the Stage for Structured Populations.- Island Models.- Island Models: Empirical Properties.- Lattice Cellular Models.- Lattice Cellular Models: Empirical Properties.- Random and Irregular Cellular Populations.- Coevolutionary Structured Models.- Some Nonconventional Models.