In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given. This book breaks with 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-readable 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.

A theory of representations is not taken as given, but is developed and applied to real-world problems of commercial importance Considers basic concepts of representations, such as redundancy, scaling and locality Completely overhauled 2nd edition of this easily readable and successful book

Autorentext

Prof. Rothlauf completed his PhD thesis in 2001 at the University of Bayreuth, supervised by David E. Goldberg of the Illinois Genetic Algorithms Laboratory (IlliGAL). His particular areas of interest are optimization and heuristics, particulary in the areas of transportation, logistics and business information systems. He chaired the main international conference on evolutionary computing, ACM GECCO, in 2009, and has co-organized many related conferences and workshops; he has also acted as guest editor for related journal special issues; and he published the book "Representations for Genetic and Evolutionary Algorithms" in 2002, this was published in a second edition in 2006.

Inhalt
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.