A Performance Analysis of Self-\(\star \) Evolutionary Algorithms on Networks with Correlated Failures

  • Rafael Nogueras
  • Carlos Cotta
Part of the Studies in Computational Intelligence book series (SCI, volume 737)


We consider the deployment of island-based evolutionary algorithms (EAs) on unstable networks whose nodes exhibit correlated failures. We use the sandpile model in order to induce such complex, correlated failures in the system. A performance analysis is conducted, comparing the results obtained in both correlated and non-correlated scenarios for increasingly large volatility rates. It is observed that simple island-based EAs have a significant performance degradation in the correlated scenario with respect to its uncorrelated counterpart. However, the use of self-\(\star \) properties (self-scaling and self-sampling in this case) allows the EA to increase its resilience in this harder scenario, leading to a much more gentle degradation profile.


Evolutionary algorithms Self-\(\star \) properties Ephemeral computing Sandpile model 



This work is supported by the Spanish Ministerio de Economía and European FEDER under Project EphemeCH (TIN2014-56494-C4-1-P)—http://ephemech.wordpress.com—and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.


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© Springer International Publishing AG 2018

Authors and Affiliations

  • Rafael Nogueras
    • 1
  • Carlos Cotta
    • 1
  1. 1.ETSI InformáticaCampus de Teatinos, Universidad de MálagaMálagaSpain

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