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Summary and Outlook

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

Abstract

Most common heuristics are restricted to static optimization problems, i.e. problems that are completely known to the optimization algorithm from the beginning. However, many real-world optimization problems are stochastic and change over time. Therefore, powerful heuristics are needed that are not only capable of finding good solutions to a single static problem, but that account for the dynamics and the uncertainty present in real world problems.

Keywords

Dynamic Environment Idle Time Change Cost Dynamic Optimization Problem Static Optimization 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 Science+Business Media New York 2002

Authors and Affiliations

  • Jürgen Branke
    • 1
  1. 1.University of KarlsruheGermany

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