[jira] [Commented] (MATH-1563) Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm

["AVIJIT BASAK (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MATH-1563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17380592#comment-17380592 ] 

AVIJIT BASAK commented on MATH-1563:
------------------------------------

I have started to document the changes in UML model. The existing model has been reversed engineered from the source code. I have proposed few alterations to the existing model as well as few additions. The alterations are colored as yellow and additions as blue and removals as red. The models are separated logically in three diagrams as attached below. I have attached the diagrams here to maintain a proper reference. I have used umlet (https://www.umlet.com/ ) for drawing the diagrams. We can start the review and design discussion based on these.

[^GA-Overview.uxf]

[^GA-Model.uxf]

[^GA-OperatorModel.uxf]

> 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-OperatorModel.uxf, GA-Overview.uxf
>
>
> 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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