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https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17368389#comment-17368389
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Gilles Sadowski commented on MATH-1563:
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{quote}current repository URL where the modularization is being done.
{quote}
It's the "master" branch:
[https://gitbox.apache.org/repos/asf?p=commons-math.git]
{quote}need access to the repository
{quote}
It's open-source. ;)
{quote}create feature branch and PR
{quote}
That, you do on GitHub (unless you don't have an account there).
If you have questions on the process, please post them to the "dev" ML.
Please note that if you create a *non*-legacy module, it should aim at a design
for the long-term.
Short term changes might be appropriate within the current package, if there
are people willing to review and help with committing your PRs. Which way to go
should be discussed on "dev@" in response to your preference (and concrete
proposals).
> Implementation of Adaptive Probability Generation Strategy for Genetic
> Algorithm
> --------------------------------------------------------------------------------
>
> Key: MATH-1563
> URL: https://issues.apache.org/jira/browse/MATH-1563
> Project: Commons Math
> Issue Type: Improvement
> Reporter: AVIJIT BASAK
> Priority: Major
>
> In Genetic Algorithm probability of crossover and mutation operation can be
> generated in an adaptive manner. Some experiment was done related to this and
> published in this article
> "https://www.ijcaonline.org/archives/volume175/number10/basak-2020-ijca-920572.pdf";.
> Currently Apache's API works on constant probability strategy. I would like
> to propose incorporation of rank based adaptive probability generation
> strategy as described in the mentioned article. This will improve the
> performance and robustness of the algorithm and would make this more suitable
> for use in higher dimensional problems like machine learning or deep learning.
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