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https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17353747#comment-17353747
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Gilles Sadowski commented on MATH-1563:
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{quote}Is there any other activity I need to perform for approval of this
enhancement.
{quote}
All enhancements are welcome; this one will be hosted in Commons Math given the
PMC did not agree to the creation of a GA dedicated component (cf. "dev" ML
archive).
Commons Math has just been partially modularized and the GA functionality is
now in package {{o.a.c.math4.legacy.genetics}}.
You should create a (non-legacy) module {{commons-math-ga}} for the GA
functionality and move the code into package {{o.a.c.math4.ga}}, refactoring it
on the way, so that it does *not* depend on any "legacy" module.
Currently, there are 2 non-legacy modules ({{commons-math-neuralnet}} and
{{commons-math-transform}}) that illustrate how refactored package should look
like: In the case of GA codes, it pretty much boils down to replacing the usage
of "legacy" exception types by a new (package-private) {{GAException}}.
> 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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