Second completely revised and substantially extendended edition is now
available!
Title:
Representations for Genetic and Evolutionary Algorithms
2nd edition 2006,
325 p. 99 illus., Hardcover
Springer
ISBN: 3-540-25059-X
http://wifo1.bwl.uni-mannheim.de/books/representations.html
Author:
Franz Rothlauf
[log in to unmask]
http://wifo1.bwl.uni-mannheim.de/rothlauf.html
Availability:
Springer:
http://www.springer.com/sgw/cda/frontpage/0,11855,1-40109-22-48660390-0,00.html
amazon:
http://www.amazon.com/gp/product/354025059X/ref=ed_oe_h/103-9019797-4066240?%5Fencoding=UTF8
About the book:
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. The second edition extends the analysis of the basic
properties of representations and introduces a new chapter on the
analysis of direct representations.
Features:
o Comprehensive coverage of representations for EAs
o New work on redundancy and locality of representations
o New work on bias of representations
o New work on direct encodings
o Includes applications to real-world communication network problems
o Completely revised and substantially extended
This book about representations develops applicable theory that allows
understanding on how representations influence the performance of
search and optimization algorithms. It shows how representations affect
the efficiency of search methods and helps people who are working in the
areas of optimization, optimal design, innovative design and
evolutionary computing to develop fast and efficient optimization
techniques for real-world problems in a systematic and theory-guided way.
|