Fix typos

Author: ahmedfgad

docs/source/pygad.rst

@@ -920,9 +920,7 @@ It accepts the following parameters:
 -  ``pop_fitness=None``: An optional parameter that accepts a list of
    the fitness values of the solutions in the population. If ``None``,
    then the ``cal_pop_fitness()`` method is called to calculate the
-   fitness values of the ``self.population``. Use 
-   ``ga_instance.last_generation_fitness`` to use latest fitness value
-   and skip recalculation of the population fitness.
+   fitness values of the population.
 
 It returns the following:
 
@@ -1039,11 +1037,10 @@ Let's discuss how to do each of these steps.
 Preparing the ``fitness_func`` Parameter 
 -----------------------------------------
 
-Even there are some steps in the genetic algorithm pipeline that 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.
@@ -1060,15 +1057,15 @@ optimization problem is single-objective or multi-objective.
    ``pygad.GA`` class.
 
 -  If the fitness function returns a ``list``, ``tuple``, or
-   ``numpy.ndarray``, then the problem is single-objective. Even if
-   there is only one element, the problem is still considered
-   multi-objective. Each element represents the fitness value of its
-   corresponding objective.
+   ``numpy.ndarray``, then the problem is multi-objective. Even if there
+   is only one element, the problem is still considered multi-objective.
+   Each element represents the fitness value of its corresponding
+   objective.
 
 Using a user-defined fitness function allows the user to freely use
 PyGAD to solve any problem by passing the appropriate fitness
-function/method. It is very important to understand the problem well for
-creating it.
+function/method. It is very important to understand the problem well
+before creating it.
 
 Let's discuss an example:
 

docs/source/pygad_more.rst

@@ -595,8 +595,8 @@ After running the code again, it will find the same result.
    [ 2.77249188 -4.06570662  0.04196872 -3.47770796 -0.57502138 -3.22775267]
    0.04872203136549972
 
-Continue without Loosing Progress
-=================================
+Continue without Losing Progress
+================================
 
 In `PyGAD
 2.18.0 <https://pygad.readthedocs.io/en/latest/releases.html#pygad-2-18-0>`__,
@@ -615,7 +615,7 @@ call to the ``run()`` method.
 4. ``self.solutions_fitness``
 
 This helps the user to continue where the last run stopped without
-loosing the values of these 4 attributes.
+losing the values of these 4 attributes.
 
 Now, the user can save the model by calling the ``save()`` method.
 

docs/source/releases.rst

@@ -1020,8 +1020,8 @@ Release Date: 9 September 2022
    generation. Another advantage happens when the instance is loaded and
    the ``run()`` method is called, as the old fitness value are shown on
    the graph alongside with the new fitness values. Read more in this
-   section: `Continue without Loosing
-   Progress <https://pygad.readthedocs.io/en/latest/pygad_more.html#continue-without-loosing-progress>`__
+   section: `Continue without Losing
+   Progress <https://pygad.readthedocs.io/en/latest/pygad_more.html#continue-without-losing-progress>`__
 
 4. Thanks `Prof. Fernando Jiménez
    Barrionuevo <http://webs.um.es/fernan>`__ (Dept. of Information and
@@ -1464,6 +1464,27 @@ Release Date 7 September 2023
     class is removed. Instead, please use the ``plot_fitness()`` if you
     did not upgrade yet.
 
+.. _pygad-321:
+
+PyGAD 3.2.1
+-----------
+
+Release Date ... 2023
+
+1. Fix a bug when multi-objective optimization is used with batch
+   fitness calculation (e.g. ``fitness_batch_size`` set to a non-zero
+   number).
+
+2. Fix a bug in the ``pygad.py`` script when finding the index of the
+   best solution. It does not work properly with multi-objective
+   optimization where ``self.best_solutions_fitness`` have multiple
+   columns.
+
+   .. code:: python
+
+                  self.best_solution_generation = numpy.where(numpy.array(
+                      self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0]
+
 PyGAD Projects at GitHub
 ========================
 

pygad/pygad.py

@@ -726,7 +726,7 @@ def __init__(self,
                                 if self.mutation_probability is None:
                                     if not self.suppress_warnings:
                                         warnings.warn(
-                                            f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
+                                            f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resulted in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
                                 mutation_num_genes = 1
 
                         elif type(mutation_percent_genes) in GA.supported_int_float_types:
@@ -745,7 +745,7 @@ def __init__(self,
                                 if mutation_num_genes == 0:
                                     if self.mutation_probability is None:
                                         if not self.suppress_warnings:
-                                            warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resutled in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
+                                            warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resulted in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
                                     mutation_num_genes = 1
                         else:
                             self.valid_parameters = False
@@ -771,7 +771,7 @@ def __init__(self,
                                     # Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.
                                     if mutation_num_genes[idx] == 0:
                                         if not self.suppress_warnings:
-                                            warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resutled in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
+                                            warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resulted in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")
                                         mutation_num_genes[idx] = 1
                                 if mutation_percent_genes[0] < mutation_percent_genes[1]:
                                     if not self.suppress_warnings: