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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Rafael Nogueras, Carlos Cotta

Erschienen in: Intelligent Distributed Computing XI

Verlag: Springer International Publishing

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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-\(\star \) 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.

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It must be noted that these so-called micro-failures are not intended to represent any real phenomenon, but are just used as a means to introduce failure interdependencies.

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Titel: A Performance Analysis of Self- Evolutionary Algorithms on Networks with Correlated Failures
verfasst von: Rafael Nogueras
Carlos Cotta
Verlag: Springer International Publishing
Buch: Intelligent Distributed Computing XI
Print ISBN: 978-3-319-66378-4

Electronic ISBN: 978-3-319-66379-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-66379-1_1

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