Evolutionary Optimization in Dynamic Environments by Jürgen Branke Kluwer Academic Publishers Volume 3 of the Book Series on Genetic Algorithms and Evolutionary Computation

You can download the frontmatter (title page, table of contents, preface, and acknowledgments) as a postscript file.
 

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FROM THE BACK COVER:

Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to

• continuously and efficiently adapt a solution to a changing environment,
• find a good trade-off between solution quality and adaptation cost,
• find robust solutions whose quality is insensitive to changes in the environment,
• find flexible solutions which are not only good but that can be easily adapted when necessary.

TABLE OF CONTENTS

Preface
1 Brief Introduction to Evolutionary Algorithms
Part I: Enabling Continuous Adaptation
2 Optimization in Dynamic Environments
3 Survey: State of the Art
4 From Memory to Self-Organization
5 Empirical Evaluation
6 Summary of Part I
Part II: Considering Adaptation Cost
7 Adaptation Cost vs. Solution Quality
Part III: Robustness and Flexibility - Precaution against Changes
8 Searching for Robust Solutions
9 From Robustness to Flexibility
10 Summary and Outlook
References
Index

OTHER VOLUMES IN THE SERIES: