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.

Autorentext

Marco Tomassini is a professor of Computer Science at the Information Systems Department of the University of Lausanne, Switzerland. He graduated in physical and chemical sciences in Mendoza, Argentina, and got a PhD degree in theoretical chemistry from the University of Perugia, Italy, working on computer simulations of condensed matter systems. His current research interests are centered around the application of biological ideas to artificial systems. He is active in evolutionary computation, especially spatially structured systems, genetic programming, and evolvable machines. He is also interested in machine learning, parallel cellular computing systems, and the dynamical properties of networked complex systems. He has been Program Chairman of several international events and has published many scientific papers and several authored and edited books in these fields.

Klappentext

Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about 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. Moreover, there is new material on non-standard networked population structures such as small-world networks.

The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.

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.