The Memory Indexing Evolutionary Algorithm for Dynamic Environments

  • Aydın Karaman
  • Şima Uyar
  • Gülşen Eryiğit
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)


There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.


Evolutionary Algorithm Environment Quality Tuner Parameter Dynamic Environment Knapsack Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Aydın Karaman
    • 1
  • Şima Uyar
    • 1
  • Gülşen Eryiğit
    • 1
  1. 1.Computer Engineering DepartmentIstanbul Technical UniversityMaslak IstanbulTurkey

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