Tuning of EMAS Parameters

  • Aleksander ByrskiEmail author
  • Marek Kisiel-Dorohinicki
Part of the Studies in Computational Intelligence book series (SCI, volume 680)


Having shown that EMAS approaches are effective in solving selected benchmark and real-life problems, it would be interesting to take an insight into the exact features of the most important mechanism of EMAS, i.e. the distributed selection based on existence of non-renewable resource. Such experiments could help to understand it and tune the computation based on this knowledge. The problem is not trivial, because EMAS, similar to other metaheuristics, utilises many parameters imposing on the user the setting dozens of degrees of freedom.


Selection Mechanism Initial Energy Graph Curvature Migration Probability Exact Feature 
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.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science, Electronics and Telecommunications, Department of Computer ScienceAGH University of Science and TechnologyKrakówPoland

Personalised recommendations