Differential Evolution for High Scale Dynamic Optimization

  • Mikołaj Raciborski
  • Krzysztof Trojanowski
  • Piotr Kaczyński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7053)


This paper studies properties of a differential evolution approach (DE) for dynamic optimization problems. An adaptive version of DE, namely the jDE algorithm has been applied to two well known benchmarks: Generalized Dynamic Benchmark Generator (GDBG) and Moving Peaks Benchmark (MPB). The experiments have been performed for different numbers of the search space dimensions starting from five until 30. The results show the influence of the problem complexity on the quality of the returned results both in case of varying and constant number of fitness function calls between subsequent changes.


Search Space Differential Evolution Evolutionary Computation Dynamic Optimization Dynamic 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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mikołaj Raciborski
    • 1
  • Krzysztof Trojanowski
    • 2
  • Piotr Kaczyński
    • 1
  1. 1.Faculty of Mathematics and Natural SciencesCardinal Stefan Wyszyński UniversityWarsawPoland
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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