Update docs

docs/source/helper.rst

@@ -0,0 +1,11 @@
+.. _pygadhelper-module:
+
+``pygad.helper`` Module
+=======================
+
+This section of the PyGAD's library documentation discusses the
+**pygad.helper** module.
+
+Yet, this module has a submodule called ``unique`` that has a class
+named ``Unique`` with some helper methods. Such methods help to check
+and fix duplicate values in the genes of a solution.

docs/source/index.rst

@@ -19,9 +19,10 @@ optimizing machine learning algorithms. It works with
 different types of crossover, mutation, and parent selection operators.
 `PyGAD <https://github.com/ahmedfgad/GeneticAlgorithmPython>`__ allows
 different types of problems to be optimized using the genetic algorithm
-by customizing the fitness function.
+by customizing the fitness function. It works with both single-objective
+and multi-objective optimization problems.
 
-.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
+.. image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
    :alt: 
 
 *Logo designed by* `Asmaa
@@ -108,6 +109,11 @@ equation.
 A very important step is to implement the fitness function that will be
 used for calculating the fitness value for each solution. Here is one.
 
+If the fitness function returns a number, then the problem is
+single-objective. If a ``list``, ``tuple``, or ``numpy.ndarray`` is
+returned, then it is a multi-objective problem (applicable even if a
+single element exists).
+
 .. code:: python
 
    def fitness_func(ga_instance, solution, solution_idx):
@@ -213,7 +219,7 @@ PyGAD's Modules
 8.  The ``visualize`` module to visualize the results.
 
 9.  The ``utils`` module contains the operators (crossover, mutation,
-    and parent selection).
+    and parent selection) and the NSGA-II code.
 
 10. The ``helper`` module has some helper functions.