Branke J. Evolutionary Optimization in Dynamic Environments

Kluwer, 2002. — 217 p.Many complex real-world optimization problems are dynamic, and stochastically change over time: new jobs are arriving continuously and have to be added to the schedule, machines may break down or wear out slowly, raw material is of changing quality, production tolerances have to be taken into account, etc.
These problems require powerful heuristics that account for the uncertainty present in the real world. Evolutionary algorithms (EAs) have proven successful in a vast number of static applications and the number of papers produced in this area is still growing fast. But they also seem to be particularly suitable for dynamic and stochastic optimization problems, not only because they draw their inspiration from the principles of natural evolution, which is a stochastic and dynamic process as well.
This book is concerned with the special intricacies due to the uncertainties in dynamic optimization problems, and provides the state of the art and latest research on how evolutionary algorithms may be applied to this kind of problems.Brief Introduction to Evolutionary Algorithms
Part I Enabling Continuous Adaptation
Optimization in Dynamic Environments
Survey: State of the Art
From Memory to Self-Organization
Empirical Evaluation
Summary of Part I
Part II Considering Adaptation Cost
Adaptation Cost vs. Solution Quality
Part III Robustness and Flexibility – Precaution against Changes
Searching for Robust Solutions
From Robustness to Flexibility
Summary and Outlook