© 2002

Evolutionary Optimization in Dynamic Environments


Part of the Genetic Algorithms and Evolutionary Computation book series (GENA, volume 3)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Brief Introduction to Evolutionary Algorithms

  3. Enabling Continuos Adaptation

    1. Front Matter
      Pages 11-11
    2. Jürgen Branke
      Pages 13-29
    3. Jürgen Branke
      Pages 31-52
    4. Jürgen Branke
      Pages 53-65
    5. Jürgen Branke
      Pages 67-98
    6. Jürgen Branke
      Pages 99-102
  4. Considering Adaptation Cost

    1. Front Matter
      Pages 103-103
  5. Robustness and Flexibility — Precaution Against Changes

    1. Front Matter
      Pages 123-123
    2. Jürgen Branke
      Pages 125-172
    3. Jürgen Branke
      Pages 173-184
    4. Jürgen Branke
      Pages 185-190
  6. Back Matter
    Pages 191-208

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.
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

Authors and affiliations

  1. 1.University of KarlsruheGermany

Bibliographic information

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