A Performance analysis of self-* evolutionary algorithms on networks with correlated failures

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Abstract

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-* 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.

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Nogueras, R., & Cotta, C. (2017). A Performance analysis of self-* evolutionary algorithms on networks with correlated failures. Studies in Computational Intelligence, 737, 3–13. https://doi.org/10.1007/978-3-319-66379-1_1

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