Table of contents
About this book
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.
Adaptation Cost Evolutionary Algorithms (EAs) Robust Solutions algorithms evolution evolutionary algorithm heuristics local search heuristics memory natural evolution optimization