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Commit 98b187b

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Merge pull request #272 from ahmedfgad/github-actions
Cite journal paper
2 parents d76c60f + 30de970 commit 98b187b

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‎README.md

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@@ -311,13 +311,13 @@ Find the book at these links:
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If you used PyGAD, please consider adding a citation to the following paper about PyGAD:
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```
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@misc{gad2021pygad,
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title={PyGAD: An Intuitive Genetic Algorithm Python Library},
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author={Ahmed Fawzy Gad},
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year={2021},
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eprint={2106.06158},
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archivePrefix={arXiv},
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primaryClass={cs.NE}
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@article{gad2023pygad,
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title={Pygad: An intuitive genetic algorithm python library},
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author={Gad, Ahmed Fawzy},
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journal={Multimedia Tools and Applications},
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pages={1--14},
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year={2023},
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publisher={Springer}
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}
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```
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‎docs/source/index.rst

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.. code::
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@misc{gad2021pygad,
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title={PyGAD: An Intuitive Genetic Algorithm Python Library},
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author={Ahmed Fawzy Gad},
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year={2021},
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eprint={2106.06158},
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archivePrefix={arXiv},
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primaryClass={cs.NE}
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@article{gad2023pygad,
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title={Pygad: An intuitive genetic algorithm python library},
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author={Gad, Ahmed Fawzy},
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journal={Multimedia Tools and Applications},
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pages={1--14},
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year={2023},
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publisher={Springer}
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}
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‎docs/source/pygad.rst

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function can be passed starting from `PyGAD
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2.16.0 <https://pygad.readthedocs.io/en/latest/releases.html#pygad-2-16-0>`__.
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Check the `User-Defined Crossover, Mutation, and Parent Selection
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Operators <https://pygad.readthedocs.io/en/latest/pygad_more.html#user-defined-crossover-mutation-and-parent-selection-operators>`__
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Operators <https://pygad.readthedocs.io/en/latest/utils.html#user-defined-crossover-mutation-and-parent-selection-operators>`__
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section for more details about building a user-defined parent
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selection function.
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Preparing the ``fitness_func`` Parameter
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-----------------------------------------
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Even though some steps in the genetic algorithm pipeline can
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work the same regardless of the problem being solved, one critical step
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is the calculation of the fitness value. There is no unique way of
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calculating the fitness value and it changes from one problem to
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another.
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Even though some steps in the genetic algorithm pipeline can work the
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same regardless of the problem being solved, one critical step is the
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calculation of the fitness value. There is no unique way of calculating
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the fitness value and it changes from one problem to another.
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PyGAD has a parameter called ``fitness_func`` that allows the user to
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specify a custom function/method to use when calculating the fitness.

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