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2018 | OriginalPaper | Chapter

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

Authors : Rafael Nogueras, Carlos Cotta

Published in: Intelligent Distributed Computing XI

Publisher: 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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next chapter Spatially Structured Evolutionary Algorithms: Graph Degree, Population Size and Convergence Speed
Footnotes
1
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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Metadata
Title
A Performance Analysis of Self- Evolutionary Algorithms on Networks with Correlated Failures
Authors
Rafael Nogueras
Carlos Cotta
Copyright Year
2018
Book
Intelligent Distributed Computing XI

Print ISBN: 978-3-319-66378-4

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

Copyright Year: 2018

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

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