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Author: ahmedfgad

README.md

@@ -13,22 +13,21 @@ The project will be extended to **train CNN using the genetic algorithm** with t
 
 # Donation
 
-You can donate via [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad). 
-
-To donate using PayPal, use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected].
+- [Credit/Debit Card](https://donate.stripe.com/eVa5kO866elKgM0144): https://donate.stripe.com/eVa5kO866elKgM0144
+- [Open Collective](https://opencollective.com/pygad): [opencollective.com/pygad](https://opencollective.com/pygad)
+- PayPal: Use either this link: [paypal.me/ahmedfgad](https://paypal.me/ahmedfgad) or the e-mail address [email protected]
+- Interac e-Transfer: Use e-mail address [email protected]
 
 # Installation
 
-To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library lives a PyPI at this page https://pypi.org/project/pygad.
+To install [PyGAD](https://pypi.org/project/pygad), simply use pip to download and install the library from [PyPI](https://pypi.org/project/pygad) (Python Package Index). The library is at PyPI at this page https://pypi.org/project/pygad.
 
-For Windows, issue the following command:
+Install PyGAD with the following command:
 
 ```python
-pip3 install pygad
+pip install pygad
 ```
 
-PyGAD is developed in Python 3.7.3 and depends on NumPy for creating and manipulating arrays and Matplotlib for creating figures. The exact NumPy version used in developing PyGAD is 1.16.4. For Matplotlib, the version is 3.1.0.
-
 To get started with PyGAD, please read the documentation at [Read The Docs](https://pygad.readthedocs.io/) https://pygad.readthedocs.io.
 
 # PyGAD Source Code
@@ -74,7 +73,7 @@ import numpy
 function_inputs = [4,-2,3.5,5,-11,-4.7]
 desired_output = 44
 
-def fitness_func(solution, solution_idx):
+def fitness_func(ga_instance, solution, solution_idx):
     output = numpy.sum(solution*function_inputs)
     fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
     return fitness