Evolutionary Optimization In Dynamic Environments (genetic Algorithms And Evolutionary Computation)

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. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms. 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. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes __Evolutionary Optimization in Dynamic Environments__ an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms. Front Matter....Pages i-xiv Brief Introduction to Evolutionary Algorithms....Pages 1-10 Front Matter....Pages 11-11 Optimization in Dynamic Environments....Pages 13-29 Survey: State of the Art....Pages 31-52 From Memory to Self-Organization....Pages 53-65 Empirical Evaluation....Pages 67-98 Summary of Part 1....Pages 99-102 Front Matter....Pages 103-103 Adaptation Cost vs. Solution Quality: Multiple Objectives....Pages 105-122 Front Matter....Pages 123-123 Searching for Robust Solutions....Pages 125-172 From Robustness to Flexibility....Pages 173-184 Summary and Outlook....Pages 185-190 Back Matter....Pages 191-208