Abstract
Recently, different evolutionary algorithms (EAs) have been analyzed in noisy environments. The most frequently used noise model for this was additive posterior noise (noise added after the fitness evaluation) taken from a Gaussian distribution. In particular, for this setting it was shown that the (μ + 1)-EA on OneMax does not scale gracefully (higher noise cannot efficiently be compensated by higher μ). In this paper we want to understand whether there is anything special about the Gaussian distribution which makes the (μ + 1)-EA not scale gracefully. We keep the setting of posterior noise, but we look at other distributions. We see that for exponential tails the (μ + 1)-EA on OneMax does also not scale gracefully, for similar reasons as in the case of Gaussian noise. On the other hand, for uniform distributions (as well as other, similar distributions) we see that the (μ + 1)-EA on OneMax does scale gracefully, indicating the importance of the noise model.
Original language | English (US) |
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Title of host publication | Parallel Problem Solving from Nature - 14th International Conference, PPSN 2016, Proceedings |
Editors | Emma Hart, Ben Paechter, Julia Handl, Manuel López-Ibáñez, Peter R. Lewis, Gabriela Ochoa |
Publisher | Springer Verlag |
Pages | 761-770 |
Number of pages | 10 |
ISBN (Print) | 9783319458229 |
DOIs | |
State | Published - 2016 |
Externally published | Yes |
Event | 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 - Edinburgh, United Kingdom Duration: Sep 17 2016 → Sep 21 2016 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9921 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016 |
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Country/Territory | United Kingdom |
City | Edinburgh |
Period | 9/17/16 → 9/21/16 |
Bibliographical note
Funding Information:The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 618091 (SAGE) and the German Research Foundation (DFG) under grant agreement no. FR 2988 (TOSU).
Publisher Copyright:
© Springer International Publishing AG 2016.
Keywords
- Evolutionary algorithm
- Noisy fitness
- Theory