PyGAD 2.18.2

Author: ahmedfgad

__init__.py

@@ -1,3 +1,3 @@
 from .pygad import * # Relative import.
 
-__version__ = "2.18.1"
+__version__ = "2.18.2"

docs/source/Footer.rst

@@ -1064,6 +1064,26 @@ Release Date: 19 September 2022
 1. A big fix when ``keep_elitism`` is used.
    https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/132
 
+.. _pygad-2182:
+
+PyGAD 2.18.2
+------------
+
+Release Date: 14 February 2023
+
+1. Remove ``numpy.int`` and ``numpy.float`` from the list of supported
+   data types.
+   https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/151
+   https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/152
+
+2. Call the ``on_crossover()`` callback function even if
+   ``crossover_type`` is ``None``.
+   https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138
+
+3. Call the ``on_mutation()`` callback function even if
+   ``mutation_type`` is ``None``.
+   https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/138
+
 PyGAD Projects at GitHub
 ========================
 

docs/source/README_pygad_ReadTheDocs.rst

@@ -1832,7 +1832,7 @@ as a seed for the random function generators.
 
 PyGAD uses random functions in these 2 libraries:
 
-1.  NumPy
+1. NumPy
 
 2. random
 
@@ -2261,7 +2261,7 @@ gene's value.
    def mutation_func(offspring, ga_instance):
 
        for chromosome_idx in range(offspring.shape[0]):
-           random_gene_idx = numpy.random.choice(range(offspring.shape[0]))
+           random_gene_idx = numpy.random.choice(range(offspring.shape[1]))
 
            offspring[chromosome_idx, random_gene_idx] += numpy.random.random()
 

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Ahmed Fawzy Gad'
 
 # The full version, including alpha/beta/rc tags
-release = '2.18.1'
+release = '2.18.2'
 
 master_doc = 'index'
 

docs/source/index.rst

@@ -21,11 +21,10 @@ different types of crossover, mutation, and parent selection operators.
 different types of problems to be optimized using the genetic algorithm
 by customizing the fitness function.
 
-
 .. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
    :alt: 
 
-*Logo designed by*\ `Asmaa
+*Logo designed by* `Asmaa
 Kabil <https://www.linkedin.com/in/asmaa-kabil-9901b7b6>`__
 
 Besides building the genetic algorithm, it builds and optimizes machine
@@ -75,8 +74,6 @@ 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.
 
-.. _header-n69:
-
 Quick Start
 ===========
 
@@ -186,8 +183,6 @@ solution found by PyGAD can be accessed.
 There is more to do using PyGAD. Read its documentation to explore the
 features of PyGAD.
 
-.. _header-n88:
-
 PyGAD's Modules
 ===============
 
@@ -215,27 +210,20 @@ PyGAD's Modules
 The documentation discusses each of these modules.
 
 PyGAD Citation - Bibtex Formatted
-==============
+=================================
 
 If you used PyGAD, please consider citing its paper with the following
 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}
-
    }
 
 

pygad.py

@@ -1369,8 +1369,10 @@ def run(self):
                 else:
                     self.last_generation_offspring_crossover = self.crossover(self.last_generation_parents,
                                                                               offspring_size=(self.num_offspring, self.num_genes))
-                if not (self.on_crossover is None):
-                    self.on_crossover(self, self.last_generation_offspring_crossover)
+
+            # PyGAD 2.18.2 // The on_crossover() callback function is called even if crossover_type is None.
+            if not (self.on_crossover is None):
+                self.on_crossover(self, self.last_generation_offspring_crossover)
 
             # If self.mutation_type=None, then no mutation is applied and thus no changes are applied to the offspring created using the crossover operation. The offspring will be used unchanged in the next generation.
             if self.mutation_type is None:
@@ -1381,8 +1383,10 @@ def run(self):
                     self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover, self)
                 else:
                     self.last_generation_offspring_mutation = self.mutation(self.last_generation_offspring_crossover)
-                if not (self.on_mutation is None):
-                    self.on_mutation(self, self.last_generation_offspring_mutation)
+
+            # PyGAD 2.18.2 // The on_mutation() callback function is called even if mutation_type is None.
+            if not (self.on_mutation is None):
+                self.on_mutation(self, self.last_generation_offspring_mutation)
 
             # Update the population attribute according to the offspring generated.
             if self.keep_elitism == 0:

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(  
     name="pygad",  
-    version="2.18.1",  
+    version="2.18.2",  
     author="Ahmed Fawzy Gad",
     install_requires=["numpy", "matplotlib",],
     author_email="ahmed.f.gad@gmail.com",