PyGAD 3.0.0 parallel processing

There was an issue when using the `parallel_processing` parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the `predict()` method gives incorrect and weird results when more than 1 thread is used. ahmedfgad/GeneticAlgorithmPython#145 ahmedfgad/TorchGA#5 #6. Thanks to this StackOverflow answer https://stackoverflow.com/a/75606666/5426539.

README.md

@@ -21,9 +21,10 @@ pip3 install pygad
 
 # 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 ahmed.f.gad@gmail.com.
+- [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 ahmed.f.gad@gmail.com
+- Interac e-Transfer: Use e-mail address ahmed.f.gad@gmail.com
 
 # Installation
 
@@ -80,7 +81,7 @@ 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
@@ -162,7 +163,7 @@ import pygad.kerasga
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instance, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,
@@ -243,7 +244,7 @@ import pygad.kerasga
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instance, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     model_weights_matrix = pygad.kerasga.model_weights_as_matrix(model=model,

__init__.py

@@ -1,3 +1,3 @@
 from .kerasga import *
 
-__version__ = "1.1.0"
+__version__ = "1.2.0"

keras_pygad_XOR_classification.py

@@ -3,7 +3,7 @@
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instanse, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     predictions = pygad.kerasga.predict(model=model,

keras_pygad_image_classification_CNN.py

@@ -3,7 +3,7 @@
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instanse, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     predictions = pygad.kerasga.predict(model=model,

keras_pygad_image_classification_Dense.py

@@ -3,7 +3,7 @@
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instanse, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     predictions = pygad.kerasga.predict(model=model,

keras_pygad_regression.py

@@ -3,7 +3,7 @@
 import numpy
 import pygad
 
-def fitness_func(solution, sol_idx):
+def fitness_func(ga_instanse, solution, sol_idx):
     global data_inputs, data_outputs, keras_ga, model
 
     predictions = pygad.kerasga.predict(model=model,

kerasga.py

@@ -1,5 +1,6 @@
 import copy
 import numpy
+import tensorflow.keras
 
 def model_weights_as_vector(model):
     weights_vector = []
@@ -40,8 +41,9 @@ def predict(model, solution, data):
     # Fetch the parameters of the best solution.
     solution_weights = model_weights_as_matrix(model=model,
                                                weights_vector=solution)
-    model.set_weights(solution_weights)
-    predictions = model.predict(data)
+    _model = tensorflow.keras.models.clone_model(model)
+    _model.set_weights(solution_weights)
+    predictions = _model.predict(data)
 
     return predictions