http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Representations for Genetic and Evolutionary Algorithms
In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance. The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently. The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.
Representations for Genetic and Evolutionary Algorithms -- Three Elements of a Theory of Representations -- Time-Quality Framework for a Theory-Based Analysis and Design of Representations -- Analysis of Binary Representations of Integers -- Analysis and Design of Representations for Trees -- Analysis and Design of Search Operators for Trees -- Performance of Genetic and Evolutionary Algorithms on Tree Problems -- Summary and Conclusions.
Appl.Mathematics/Computational Methods of Engineering
Artificial Intelligence (incl. Robotics)