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  1. Commons Math
  2. MATH-1563

Implementation of Adaptive Probability Generation Strategy for Genetic Algorithm

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    • Improvement
    • Status: Open
    • Major
    • Resolution: Unresolved
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    Description

      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.

      Attachments

        1. GA-Overview.uxf
          7 kB
          AVIJIT BASAK
        2. GA-Model.uxf
          10 kB
          AVIJIT BASAK
        3. GA-OperatorModel.uxf
          14 kB
          AVIJIT BASAK
        4. GA-Overview.uxf
          8 kB
          AVIJIT BASAK
        5. GA-Model.uxf
          12 kB
          AVIJIT BASAK
        6. GA-OperatorModel.uxf
          14 kB
          AVIJIT BASAK
        7. chromosome hierarchy.png
          80 kB
          AVIJIT BASAK

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              avijit.basak AVIJIT BASAK
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