Spatially Structured Evolutionary Algorithms

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.

• algorithm
• algorithms
• evolution
• evolutionary algorithm
• evolutionary computation
• heuristics
• learning
• machine learning
• optimization
• algorithm analysis and problem complexity

From the reviews:

"The focus of this very interesting book is on evolutionary heuristic algorithms and in particular those evolutionary algorithms that are ‘spatially structured’ which exhibit slightly different properties than standard evolutionary algorithms. The book starts with a very useful introduction on graphs and cellular models, where most of the terminology is laid out. … The book concludes with a detailed list of references and an index." (Efstratios Rappos, Zentralblatt MATH, Vol. 1089 (15), 2006)

"The strong points of the book is that it provides many variations in the structure of any EA, thus offering numerous options to consider when implementing an EA. This is very useful for anyone using EAs in practice. The book also contains a wealth of empirical data comparing the performance of these many variations … . The book is quite comprehensive and contains a wealth of very valuable information for any EA practitioner. The references are also very nice and varied." (Franklin A. Mendivil, Mathematical Reviews, Issue 2007 k)

• Information Systems Department (INFORGE), University of Lausanne, Lausanne, Switzerland

  Marco Tomassini

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.

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