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Merge pull request #272 from ahmedfgad/github-actions
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Cite journal paper
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ahmedfgad committed Feb 2, 2024
2 parents d76c60f + 30de970 commit 98b187b
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14 changes: 7 additions & 7 deletions README.md
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Expand Up @@ -311,13 +311,13 @@ Find the book at these links:
If you used PyGAD, please consider adding a citation to the following paper about PyGAD:

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

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14 changes: 7 additions & 7 deletions docs/source/index.rst
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Expand Up @@ -228,13 +228,13 @@ details:

.. code::
@misc{gad2021pygad,
title={PyGAD: An Intuitive Genetic Algorithm Python Library},
author={Ahmed Fawzy Gad},
year={2021},
eprint={2106.06158},
archivePrefix={arXiv},
primaryClass={cs.NE}
@article{gad2023pygad,
title={Pygad: An intuitive genetic algorithm python library},
author={Gad, Ahmed Fawzy},
journal={Multimedia Tools and Applications},
pages={1--14},
year={2023},
publisher={Springer}
}
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11 changes: 5 additions & 6 deletions docs/source/pygad.rst
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Expand Up @@ -119,7 +119,7 @@ The ``pygad.GA`` class constructor supports the following parameters:
function can be passed starting from `PyGAD
2.16.0 <https://pygad.readthedocs.io/en/latest/releases.html#pygad-2-16-0>`__.
Check the `User-Defined Crossover, Mutation, and Parent Selection
Operators <https://pygad.readthedocs.io/en/latest/pygad_more.html#user-defined-crossover-mutation-and-parent-selection-operators>`__
Operators <https://pygad.readthedocs.io/en/latest/utils.html#user-defined-crossover-mutation-and-parent-selection-operators>`__
section for more details about building a user-defined parent
selection function.

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Preparing the ``fitness_func`` Parameter
-----------------------------------------

Even though some steps in the genetic algorithm pipeline can
work the same regardless of the problem being solved, one critical step
is the calculation of the fitness value. There is no unique way of
calculating the fitness value and it changes from one problem to
another.
Even though some steps in the genetic algorithm pipeline can work the
same regardless of the problem being solved, one critical step is the
calculation of the fitness value. There is no unique way of calculating
the fitness value and it changes from one problem to another.

PyGAD has a parameter called ``fitness_func`` that allows the user to
specify a custom function/method to use when calculating the fitness.
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