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Author: ahmedfgad

docs/source/README_pygad_ReadTheDocs.rst

@@ -9,7 +9,7 @@ This section of the PyGAD's library documentation discusses the
 Using the ``pygad`` module, instances of the genetic algorithm can be
 created, run, saved, and loaded.
 
-.. _header-n3:
+.. _header-n4:
 
 ``pygad.GA`` Class
 ==================
@@ -19,7 +19,7 @@ class named ``GA`` for building the genetic algorithm. The constructor,
 methods, function, and attributes within the class are discussed in this
 section.
 
-.. _header-n5:
+.. _header-n6:
 
 ``__init__()``
 --------------
@@ -142,7 +142,7 @@ parameter (i.e. ``init_range_low`` is not needed to be lower than
 The parameters are validated within the constructor. If at least a
 parameter is not validated, an exception is thrown.
 
-.. _header-n48:
+.. _header-n49:
 
 Other Instance Attributes & Methods
 -----------------------------------
@@ -154,7 +154,7 @@ attributes and methods added to the instances of the **pygad.GA** class:
 
 The next 2 subsections list such attributes and methods.
 
-.. _header-n51:
+.. _header-n52:
 
 Other Attributes
 ~~~~~~~~~~~~~~~~
@@ -179,7 +179,7 @@ Other Attributes
    fitness value is reached. It is only assigned the generation number
    after the ``run()`` method completes. Otherwise, its value is -1.
 
-.. _header-n67:
+.. _header-n68:
 
 Other Methods
 ~~~~~~~~~~~~~
@@ -203,7 +203,7 @@ Other Methods
 The next sections discuss the methods available in the **pygad.GA**
 class.
 
-.. _header-n78:
+.. _header-n79:
 
 ``initialize_population()``
 ---------------------------
@@ -230,7 +230,7 @@ This method assigns the values of the following 3 instance attributes:
 
 3. ``initial_population``: Keeping the initial population.
 
-.. _header-n94:
+.. _header-n95:
 
 ``cal_pop_fitness()``
 ---------------------
@@ -244,7 +244,7 @@ constructor for each solution.
 
 It returns an array of the solutions' fitness values.
 
-.. _header-n98:
+.. _header-n99:
 
 ``run()``
 ---------
@@ -288,7 +288,7 @@ After the ``run()`` method completes, the following takes place:
 
 -  The ``run_completed`` attribute is set to ``True``.
 
-.. _header-n117:
+.. _header-n118:
 
 Parent Selection Methods
 ------------------------
@@ -306,49 +306,49 @@ All of such methods return an array of the selected parents.
 
 The next subsections list the supported methods for parent selection.
 
-.. _header-n126:
+.. _header-n127:
 
 ``steady_state_selection()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents using the steady-state selection technique.
 
-.. _header-n128:
+.. _header-n129:
 
 ``rank_selection()``
 ~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents using the rank selection technique.
 
-.. _header-n130:
+.. _header-n131:
 
 ``random_selection()``
 ~~~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents randomly.
 
-.. _header-n132:
+.. _header-n133:
 
 ``tournament_selection()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents using the tournament selection technique.
 
-.. _header-n134:
+.. _header-n135:
 
 ``roulette_wheel_selection()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents using the roulette wheel selection technique.
 
-.. _header-n136:
+.. _header-n137:
 
 ``stochastic_universal_selection()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Selects the parents using the stochastic universal selection technique.
 
-.. _header-n138:
+.. _header-n139:
 
 Crossover Methods
 -----------------
@@ -365,31 +365,31 @@ All of such methods return an array of the produced offspring.
 
 The next subsections list the supported methods for crossover.
 
-.. _header-n147:
+.. _header-n148:
 
 ``single_point_crossover()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Applies the single-point crossover. It selects a point randomly at which
 crossover takes place between the pairs of parents.
 
-.. _header-n149:
+.. _header-n150:
 
 ``two_points_crossover()``
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Applies the 2 points crossover. It selects the 2 points randomly at
 which crossover takes place between the pairs of parents.
 
-.. _header-n151:
+.. _header-n152:
 
 ``uniform_crossover()``
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 Applies the uniform crossover. For each gene, a parent out of the 2
 mating parents is selected randomly and the gene is copied from it.
 
-.. _header-n153:
+.. _header-n154:
 
 Mutation Methods
 ----------------
@@ -403,7 +403,7 @@ All of such methods return an array of the mutated offspring.
 
 The next subsections list the supported methods for mutation.
 
-.. _header-n160:
+.. _header-n161:
 
 ``random_mutation()``
 ~~~~~~~~~~~~~~~~~~~~~
@@ -417,31 +417,31 @@ specified by the 2 attributes ``random_mutation_min_val`` and
 ``random_mutation_max_val``. The random value is added to the selected
 gene.
 
-.. _header-n163:
+.. _header-n164:
 
 ``swap_mutation()``
 ~~~~~~~~~~~~~~~~~~~
 
 Applies the swap mutation which interchanges the values of 2 randomly
 selected genes.
 
-.. _header-n165:
+.. _header-n166:
 
 ``inversion_mutation()``
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 Applies the inversion mutation which selects a subset of genes and
 inverts them.
 
-.. _header-n167:
+.. _header-n168:
 
 ``scramble_mutation()``
 ~~~~~~~~~~~~~~~~~~~~~~~
 
 Applies the scramble mutation which selects a subset of genes and
 shuffles their order randomly.
 
-.. _header-n169:
+.. _header-n170:
 
 ``best_solution()``
 -------------------
@@ -459,7 +459,7 @@ following is returned:
 -  ``best_match_idx``: Index of the best solution in the current
    population.
 
-.. _header-n179:
+.. _header-n180:
 
 ``plot_result()``
 -----------------
@@ -470,7 +470,7 @@ generation.
 
 If no generation is completed (at least 1), an exception is raised.
 
-.. _header-n182:
+.. _header-n183:
 
 ``save()``
 ----------
@@ -482,7 +482,7 @@ Accepts the following parameter:
 -  ``filename``: Name of the file to save the instance. No extension is
    needed.
 
-.. _header-n188:
+.. _header-n189:
 
 Functions in ``pygad``
 ======================
@@ -491,7 +491,7 @@ Besides the methods available in the **pygad.GA** class, this section
 discusses the functions available in pygad. Up to this time, there is
 only a single function named ``load()``.
 
-.. _header-n190:
+.. _header-n191:
 
 ``pygad.load()``
 ----------------
@@ -508,7 +508,7 @@ Accepts the following parameter:
 
 Returns the genetic algorithm instance.
 
-.. _header-n197:
+.. _header-n198:
 
 Steps to Use ``pygad``
 ======================
@@ -533,7 +533,7 @@ To use the ``pygad`` module, here is a summary of the required steps:
 
 Let's discuss how to do each of these steps.
 
-.. _header-n217:
+.. _header-n218:
 
 Preparing the ``fitness_func`` Parameter 
 -----------------------------------------
@@ -593,7 +593,7 @@ an exception is thrown.
 By creating this function, you almost did an awesome step towards using
 PyGAD.
 
-.. _header-n233:
+.. _header-n234:
 
 Preparing Other Parameters
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -621,7 +621,7 @@ Here is an example for preparing the other parameters:
    mutation_type = "random"
    mutation_percent_genes = 10
 
-.. _header-n236:
+.. _header-n237:
 
 The ``callback_generation`` Parameter
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -654,7 +654,7 @@ generation.
 After the parameters are prepared, we can import PyGAD and build an
 instance of the **pygad.GA** class.
 
-.. _header-n242:
+.. _header-n243:
 
 Import the ``pygad``
 --------------------
@@ -668,7 +668,7 @@ The next step is to import PyGAD as follows:
 The **pygad.GA** class holds the implementation of all methods for
 running the genetic algorithm.
 
-.. _header-n246:
+.. _header-n247:
 
 Create an Instance of the ``pygad.GA`` Class
 --------------------------------------------
@@ -692,7 +692,7 @@ for creating the initial population.
                           mutation_type=mutation_type,
                           mutation_percent_genes=mutation_percent_genes)
 
-.. _header-n249:
+.. _header-n250:
 
 Run the Genetic Algorithm
 -------------------------
@@ -716,7 +716,7 @@ by doing the following tasks:
 
 4. Repeat the process for the specified number of generations.
 
-.. _header-n262:
+.. _header-n263:
 
 Plotting Results
 ----------------
@@ -732,7 +732,7 @@ generations.
 .. figure:: https://user-images.githubusercontent.com/16560492/78830005-93111d00-79e7-11ea-9d8e-a8d8325a6101.png
    :alt: 
 
-.. _header-n266:
+.. _header-n267:
 
 Information about the Best Solution
 -----------------------------------
@@ -762,7 +762,7 @@ fitness** is reached could be fetched.
    if ga_instance.best_solution_generation != -1:
        print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
 
-.. _header-n278:
+.. _header-n279:
 
 Saving & Loading the Results
 ----------------------------
@@ -795,7 +795,7 @@ any property.
 
    print(loaded_ga_instance.best_solution())
 
-.. _header-n285:
+.. _header-n286:
 
 Crossover, Mutation, and Parent Selection
 =========================================
@@ -805,7 +805,7 @@ the crossover & mutation operators. More features will be added in the
 future. To ask for a new feature, please check the **Ask for Feature**
 section.
 
-.. _header-n287:
+.. _header-n288:
 
 Supported Crossover Operations
 ------------------------------
@@ -819,7 +819,7 @@ The supported crossover operations at this time are:
 
 3. Uniform: Implemented using the ``uniform_crossover()`` method.
 
-.. _header-n296:
+.. _header-n297:
 
 Supported Mutation Operations
 -----------------------------
@@ -834,7 +834,7 @@ The supported mutation operations at this time are:
 
 4. Scramble: Implemented using the ``scramble_mutation()`` method.
 
-.. _header-n307:
+.. _header-n308:
 
 Supported Parent Selection Operations
 -------------------------------------
@@ -856,15 +856,15 @@ The supported parent selection techniques at this time are:
 
 6. Tournament: Implemented using the ``tournament_selection()`` method.
 
-.. _header-n322:
+.. _header-n323:
 
 Examples
 ========
 
 This section gives the complete code of some examples that use
 ``pygad``. Each subsection builds a different example.
 
-.. _header-n324:
+.. _header-n325:
 
 Linear Model Optimization
 -------------------------