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https://issues.apache.org/jira/browse/MATH-1563?focusedWorklogId=724368&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-724368
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ASF GitHub Bot logged work on MATH-1563:
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Author: ASF GitHub Bot
Created on: 10/Feb/22 08:58
Start Date: 10/Feb/22 08:58
Worklog Time Spent: 10m
Work Description: coveralls edited a comment on pull request #205:
URL: https://github.com/apache/commons-math/pull/205#issuecomment-1030758298
[](https://coveralls.io/builds/46405874)
Coverage decreased (-0.006%) to 90.326% when pulling
**6f1ee0a9e44251ee9c906922166a8bbabe064aef on
avijit-basak:feature/MATH-1563-RB-1** into
**0d6c6e60f4f840f0798d5d98579c3b9c9ffac830 on
apache:feature__MATH-1563__genetic_algorithm**.
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Issue Time Tracking
-------------------
Worklog Id: (was: 724368)
Time Spent: 50m (was: 40m)
> 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
> Attachments: GA-Model.uxf, GA-Model.uxf, GA-OperatorModel.uxf,
> GA-OperatorModel.uxf, GA-Overview.uxf, GA-Overview.uxf, chromosome
> hierarchy.png
>
> Time Spent: 50m
> Remaining Estimate: 0h
>
> 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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