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Software . 2026
License: CC BY
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Software . 2026
License: CC BY
Data sources: Datacite
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Software . 2026
License: CC BY
Data sources: Datacite
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CMA-ES/pycma: r4.4.3

Authors: Nikolaus Hansen; yoshihikoueno; ARF1; Sait Cakmak; Gabi Kadlecová; Guillermo Abad López; Kento Nozawa; +5 Authors

CMA-ES/pycma: r4.4.3

Abstract

Release 4.4.3 Addressing issue 231, failures in corner cases with large population size, by increasing the step-size damping of CSA and TPA. This seems also to improve the performance on bbob-f24 in 10 and 20-D while worsening the performance on bbob-f23 in 10 and 40-D. Provide option 'TPA_dampfac' analogous to 'CSA_dampfac'. Plots now show the current best solution and the distribution mean in two subplots. New: Provide the two (by far) most useful statistical tests with a tidy interface in cma.utilities.math.test... Provide a more_algorithms sub-package containing purecma and CompactGA. Provide a provisional experimentation module (requires import cma.experimentation). A few smaller fixes and improvements. Full Changelog: https://github.com/CMA-ES/pycma/compare/r4.4.2...r4.4.3

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
85
Top 1%
Top 1%
Top 1%