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Completely Derandomized Self-Adaptation in Evolution Strategies | MIT Press Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This paper presents two methods for self-adaptation of the mutation distribution: derandomization and cumulation.
2. The underlying objective of mutative strategy parameter control is to favor previously selected mutation steps in the future, leading to a completely derandomized self-adaptation scheme called covariance matrix adaptation (CMA).
3. Simulations on various test functions reveal that CMA can lead to a speed up factor of several orders of magnitude on badly scaled, non-separable functions and a speed up factor of three to ten on moderately mis-scaled functions.
Article analysis:
This article provides an overview of two methods for self-adaptation of the mutation distribution - derandomization and cumulation - as well as their application in the form of covariance matrix adaptation (CMA). The article is well written and provides clear explanations for each concept discussed. It also includes simulations on various test functions which demonstrate the effectiveness of CMA in improving search performance.
The article does not appear to be biased or one-sided, as it presents both sides equally and does not make any unsupported claims or omit any points of consideration. Furthermore, it provides evidence for its claims in the form of simulations and results from these tests are presented objectively without any promotional content or partiality. The article also notes potential risks associated with using CMA, such as overfitting or premature convergence, which suggests that all possible risks have been taken into account when writing this article.
In conclusion, this article appears to be trustworthy and reliable due to its balanced presentation and lack of bias or unsupported claims.