Evolutionary Optimization in Dynamic Environments

1. Front Matter

   Pages i-xiv

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2. Brief Introduction to Evolutionary Algorithms

   1. Brief Introduction to Evolutionary Algorithms

      • Jürgen Branke

      Pages 1-10

3. Enabling Continuos Adaptation

   1. Front Matter

      Pages 11-11

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   2. Optimization in Dynamic Environments

      • Jürgen Branke

      Pages 13-29

   3. Survey: State of the Art

      • Jürgen Branke

      Pages 31-52

   4. From Memory to Self-Organization

      • Jürgen Branke

      Pages 53-65

   5. Empirical Evaluation

      • Jürgen Branke

      Pages 67-98

   6. Summary of Part 1

      • Jürgen Branke

      Pages 99-102

4. Considering Adaptation Cost

   1. Front Matter

      Pages 103-103

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   2. Adaptation Cost vs. Solution Quality: Multiple Objectives

      • Jürgen Branke

      Pages 105-122

5. Robustness and Flexibility — Precaution Against Changes

   1. Front Matter

      Pages 123-123

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   2. Searching for Robust Solutions

      • Jürgen Branke

      Pages 125-172

   3. From Robustness to Flexibility

      • Jürgen Branke

      Pages 173-184

   4. Summary and Outlook

      • Jürgen Branke

      Pages 185-190

6. Back Matter

   Pages 191-208

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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.

• Adaptation Cost
• Evolutionary Algorithms (EAs)
• Robust Solutions
• algorithms
• evolution
• evolutionary algorithm
• heuristics
• local search heuristics
• memory
• natural evolution
• optimization

• University of Karlsruhe, Germany

  Jürgen Branke

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