Release History
===============
.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
:alt:
.. _pygad-1017:
PyGAD 1.0.17
------------
Release Date: 15 April 2020
1. The **pygad.GA** class accepts a new argument named ``fitness_func``
which accepts a function to be used for calculating the fitness
values for the solutions. This allows the project to be customized to
any problem by building the right fitness function.
.. _pygad-1020:
PyGAD 1.0.20
------------
Release Date: 4 May 2020
1. The **pygad.GA** attributes are moved from the class scope to the
instance scope.
2. Raising an exception for incorrect values of the passed parameters.
3. Two new parameters are added to the **pygad.GA** class constructor
(``init_range_low`` and ``init_range_high``) allowing the user to
customize the range from which the genes values in the initial
population are selected.
4. The code object ``__code__`` of the passed fitness function is
checked to ensure it has the right number of parameters.
.. _pygad-200:
PyGAD 2.0.0
-----------
Release Date: 13 May 2020
1. The fitness function accepts a new argument named ``sol_idx``
representing the index of the solution within the population.
2. A new parameter to the **pygad.GA** class constructor named
``initial_population`` is supported to allow the user to use a custom
initial population to be used by the genetic algorithm. If not None,
then the passed population will be used. If ``None``, then the
genetic algorithm will create the initial population using the
``sol_per_pop`` and ``num_genes`` parameters.
3. The parameters ``sol_per_pop`` and ``num_genes`` are optional and set
to ``None`` by default.
4. A new parameter named ``callback_generation`` is introduced in the
**pygad.GA** class constructor. It accepts a function with a single
parameter representing the **pygad.GA** class instance. This function
is called after each generation. This helps the user to do
post-processing or debugging operations after each generation.
.. _pygad-210:
PyGAD 2.1.0
-----------
Release Date: 14 May 2020
1. The ``best_solution()`` method in the **pygad.GA** class returns a
new output representing the index of the best solution within the
population. Now, it returns a total of 3 outputs and their order is:
best solution, best solution fitness, and best solution index. Here
is an example:
.. code:: python
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Parameters of the best solution :", solution)
print("Fitness value of the best solution :", solution_fitness, "\n")
print("Index of the best solution :", solution_idx, "\n")
1. | A new attribute named ``best_solution_generation`` is added to the
instances of the **pygad.GA** class. it holds the generation number
at which the best solution is reached. It is only assigned the
generation number after the ``run()`` method completes. Otherwise,
its value is -1.
| Example:
.. code:: python
print("Best solution reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
1. The ``best_solution_fitness`` attribute is renamed to
``best_solutions_fitness`` (plural solution).
2. Mutation is applied independently for the genes.
.. _pygad-221:
PyGAD 2.2.1
-----------
Release Date: 17 May 2020
1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and
training neural networks with the genetic algorithm.
.. _pygad-222:
PyGAD 2.2.2
-----------
Release Date: 18 May 2020
1. The initial value of the ``generations_completed`` attribute of
instances from the pygad.GA class is ``0`` rather than ``None``.
2. An optional bool parameter named ``mutation_by_replacement`` is added
to the constructor of the pygad.GA class. It works only when the
selected type of mutation is random (``mutation_type="random"``). In
this case, setting ``mutation_by_replacement=True`` means replace the
gene by the randomly generated value. If ``False``, then it has no
effect and random mutation works by adding the random value to the
gene. This parameter should be used when the gene falls within a
fixed range and its value must not go out of this range. Here are
some examples:
Assume there is a gene with the value 0.5.
If ``mutation_type="random"`` and ``mutation_by_replacement=False``,
then the generated random value (e.g. 0.1) will be added to the gene
value. The new gene value is **0.5+0.1=0.6**.
If ``mutation_type="random"`` and ``mutation_by_replacement=True``, then
the generated random value (e.g. 0.1) will replace the gene value. The
new gene value is **0.1**.
1. ``None`` value could be assigned to the ``mutation_type`` and
``crossover_type`` parameters of the pygad.GA class constructor. When
``None``, this means the step is bypassed and has no action.
.. _pygad-230:
PyGAD 2.3.0
-----------
Release date: 1 June 2020
1. A new module named ``pygad.cnn`` is supported for building
convolutional neural networks.
2. A new module named ``pygad.gacnn`` is supported for training
convolutional neural networks using the genetic algorithm.
3. The ``pygad.plot_result()`` method has 3 optional parameters named
``title``, ``xlabel``, and ``ylabel`` to customize the plot title,
x-axis label, and y-axis label, respectively.
4. The ``pygad.nn`` module supports the softmax activation function.
5. The name of the ``pygad.nn.predict_outputs()`` function is changed to
``pygad.nn.predict()``.
6. The name of the ``pygad.nn.train_network()`` function is changed to
``pygad.nn.train()``.
.. _pygad-240:
PyGAD 2.4.0
-----------
Release date: 5 July 2020
1. A new parameter named ``delay_after_gen`` is added which accepts a
non-negative number specifying the time in seconds to wait after a
generation completes and before going to the next generation. It
defaults to ``0.0`` which means no delay after the generation.
2. The passed function to the ``callback_generation`` parameter of the
pygad.GA class constructor can terminate the execution of the genetic
algorithm if it returns the string ``stop``. This causes the
``run()`` method to stop.
One important use case for that feature is to stop the genetic algorithm
when a condition is met before passing though all the generations. The
user may assigned a value of 100 to the ``num_generations`` parameter of
the pygad.GA class constructor. Assuming that at generation 50, for
example, a condition is met and the user wants to stop the execution
before waiting the remaining 50 generations. To do that, just make the
function passed to the ``callback_generation`` parameter to return the
string ``stop``.
Here is an example of a function to be passed to the
``callback_generation`` parameter which stops the execution if the
fitness value 70 is reached. The value 70 might be the best possible
fitness value. After being reached, then there is no need to pass
through more generations because no further improvement is possible.
.. code:: python
def func_generation(ga_instance):
if ga_instance.best_solution()[1] >= 70:
return "stop"
.. _pygad-250:
PyGAD 2.5.0
-----------
Release date: 19 July 2020
1. | 2 new optional parameters added to the constructor of the
``pygad.GA`` class which are ``crossover_probability`` and
``mutation_probability``.
| While applying the crossover operation, each parent has a random
value generated between 0.0 and 1.0. If this random value is less
than or equal to the value assigned to the
``crossover_probability`` parameter, then the parent is selected
for the crossover operation.
| For the mutation operation, a random value between 0.0 and 1.0 is
generated for each gene in the solution. If this value is less than
or equal to the value assigned to the ``mutation_probability``,
then this gene is selected for mutation.
2. A new optional parameter named ``linewidth`` is added to the
``plot_result()`` method to specify the width of the curve in the
plot. It defaults to 3.0.
3. Previously, the indices of the genes selected for mutation was
randomly generated once for all solutions within the generation.
Currently, the genes' indices are randomly generated for each
solution in the population. If the population has 4 solutions, the
indices are randomly generated 4 times inside the single generation,
1 time for each solution.
4. Previously, the position of the point(s) for the single-point and
two-points crossover was(were) randomly selected once for all
solutions within the generation. Currently, the position(s) is(are)
randomly selected for each solution in the population. If the
population has 4 solutions, the position(s) is(are) randomly
generated 4 times inside the single generation, 1 time for each
solution.
5. A new optional parameter named ``gene_space`` as added to the
``pygad.GA`` class constructor. It is used to specify the possible
values for each gene in case the user wants to restrict the gene
values. It is useful if the gene space is restricted to a certain
range or to discrete values. For more information, check the `More
about the ``gene_space``
Parameter `__
section. Thanks to `Prof. Tamer A.
Farrag `__ for requesting this useful
feature.
.. _pygad-260:
PyGAD 2.6.0
-----------
Release Date: 6 August 2020
1. A bug fix in assigning the value to the ``initial_population``
parameter.
2. A new parameter named ``gene_type`` is added to control the gene
type. It can be either ``int`` or ``float``. It has an effect only
when the parameter ``gene_space`` is ``None``.
3. 7 new parameters that accept callback functions: ``on_start``,
``on_fitness``, ``on_parents``, ``on_crossover``, ``on_mutation``,
``on_generation``, and ``on_stop``.
.. _pygad-270:
PyGAD 2.7.0
-----------
Release Date: 11 September 2020
1. The ``learning_rate`` parameter in the ``pygad.nn.train()`` function
defaults to **0.01**.
2. Added support of building neural networks for regression using the
new parameter named ``problem_type``. It is added as a parameter to
both ``pygad.nn.train()`` and ``pygad.nn.predict()`` functions. The
value of this parameter can be either **classification** or
**regression** to define the problem type. It defaults to
**classification**.
3. The activation function for a layer can be set to the string
``"None"`` to refer that there is no activation function at this
layer. As a result, the supported values for the activation function
are ``"sigmoid"``, ``"relu"``, ``"softmax"``, and ``"None"``.
To build a regression network using the ``pygad.nn`` module, just do the
following:
1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and
``pygad.nn.predict()`` functions to the string ``"regression"``.
2. Set the activation function for the output layer to the string
``"None"``. This sets no limits on the range of the outputs as it
will be from ``-infinity`` to ``+infinity``. If you are sure that all
outputs will be nonnegative values, then use the ReLU function.
Check the documentation of the ``pygad.nn`` module for an example that
builds a neural network for regression. The regression example is also
available at `this GitHub
project `__:
https://github.com/ahmedfgad/NumPyANN
To build and train a regression network using the ``pygad.gann`` module,
do the following:
1. Set the ``problem_type`` parameter in the ``pygad.nn.train()`` and
``pygad.nn.predict()`` functions to the string ``"regression"``.
2. Set the ``output_activation`` parameter in the constructor of the
``pygad.gann.GANN`` class to ``"None"``.
Check the documentation of the ``pygad.gann`` module for an example that
builds and trains a neural network for regression. The regression
example is also available at `this GitHub
project `__:
https://github.com/ahmedfgad/NeuralGenetic
To build a classification network, either ignore the ``problem_type``
parameter or set it to ``"classification"`` (default value). In this
case, the activation function of the last layer can be set to any type
(e.g. softmax).
.. _pygad-271:
PyGAD 2.7.1
-----------
Release Date: 11 September 2020
1. A bug fix when the ``problem_type`` argument is set to
``regression``.
.. _pygad-272:
PyGAD 2.7.2
-----------
Release Date: 14 September 2020
1. Bug fix to support building and training regression neural networks
with multiple outputs.
.. _pygad-280:
PyGAD 2.8.0
-----------
Release Date: 20 September 2020
1. Support of a new module named ``kerasga`` so that the Keras models
can be trained by the genetic algorithm using PyGAD.
.. _pygad-281:
PyGAD 2.8.1
-----------
Release Date: 3 October 2020
1. Bug fix in applying the crossover operation when the
``crossover_probability`` parameter is used. Thanks to `Eng. Hamada
Kassem, Research and Teaching Assistant, Construction Engineering and
Management, Faculty of Engineering, Alexandria University,
Egypt `__.
.. _pygad-290:
PyGAD 2.9.0
-----------
Release Date: 06 December 2020
1. The fitness values of the initial population are considered in the
``best_solutions_fitness`` attribute.
2. An optional parameter named ``save_best_solutions`` is added. It
defaults to ``False``. When it is ``True``, then the best solution
after each generation is saved into an attribute named
``best_solutions``. If ``False``, then no solutions are saved and the
``best_solutions`` attribute will be empty.
3. Scattered crossover is supported. To use it, assign the
``crossover_type`` parameter the value ``"scattered"``.
4. NumPy arrays are now supported by the ``gene_space`` parameter.
5. The following parameters (``gene_type``, ``crossover_probability``,
``mutation_probability``, ``delay_after_gen``) can be assigned to a
numeric value of any of these data types: ``int``, ``float``,
``numpy.int``, ``numpy.int8``, ``numpy.int16``, ``numpy.int32``,
``numpy.int64``, ``numpy.float``, ``numpy.float16``,
``numpy.float32``, or ``numpy.float64``.
.. _pygad-2100:
PyGAD 2.10.0
------------
Release Date: 03 January 2021
1. Support of a new module ``pygad.torchga`` to train PyTorch models
using PyGAD. Check `its
documentation `__.
2. Support of adaptive mutation where the mutation rate is determined
by the fitness value of each solution. Read the `Adaptive
Mutation `__
section for more details. Also, read this paper: `Libelli, S.
Marsili, and P. Alba. "Adaptive mutation in genetic algorithms."
Soft computing 4.2 (2000):
76-80. `__
3. Before the ``run()`` method completes or exits, the fitness value of
the best solution in the current population is appended to the
``best_solution_fitness`` list attribute. Note that the fitness
value of the best solution in the initial population is already
saved at the beginning of the list. So, the fitness value of the
best solution is saved before the genetic algorithm starts and after
it ends.
4. When the parameter ``parent_selection_type`` is set to ``sss``
(steady-state selection), then a warning message is printed if the
value of the ``keep_parents`` parameter is set to 0.
5. More validations to the user input parameters.
6. The default value of the ``mutation_percent_genes`` is set to the
string ``"default"`` rather than the integer 10. This change helps
to know whether the user explicitly passed a value to the
``mutation_percent_genes`` parameter or it is left to its default
one. The ``"default"`` value is later translated into the integer
10.
7. The ``mutation_percent_genes`` parameter is no longer accepting the
value 0. It must be ``>0`` and ``<=100``.
8. The built-in ``warnings`` module is used to show warning messages
rather than just using the ``print()`` function.
9. A new ``bool`` parameter called ``suppress_warnings`` is added to
the constructor of the ``pygad.GA`` class. It allows the user to
control whether the warning messages are printed or not. It defaults
to ``False`` which means the messages are printed.
10. A helper method called ``adaptive_mutation_population_fitness()`` is
created to calculate the average fitness value used in adaptive
mutation to filter the solutions.
11. The ``best_solution()`` method accepts a new optional parameter
called ``pop_fitness``. It 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 population.
.. _pygad-2101:
PyGAD 2.10.1
------------
Release Date: 10 January 2021
1. In the ``gene_space`` parameter, any ``None`` value (regardless of
its index or axis), is replaced by a randomly generated number based
on the 3 parameters ``init_range_low``, ``init_range_high``, and
``gene_type``. So, the ``None`` value in ``[..., None, ...]`` or
``[..., [..., None, ...], ...]`` are replaced with random values.
This gives more freedom in building the space of values for the
genes.
2. All the numbers passed to the ``gene_space`` parameter are casted to
the type specified in the ``gene_type`` parameter.
3. The ``numpy.uint`` data type is supported for the parameters that
accept integer values.
4. In the ``pygad.kerasga`` module, the ``model_weights_as_vector()``
function uses the ``trainable`` attribute of the model's layers to
only return the trainable weights in the network. So, only the
trainable layers with their ``trainable`` attribute set to ``True``
(``trainable=True``), which is the default value, have their weights
evolved. All non-trainable layers with the ``trainable`` attribute
set to ``False`` (``trainable=False``) will not be evolved. Thanks to
`Prof. Tamer A. Farrag `__ for
pointing about that at
`GitHub `__.
.. _pygad-2102:
PyGAD 2.10.2
------------
Release Date: 15 January 2021
1. A bug fix when ``save_best_solutions=True``. Refer to this issue for
more information:
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/25
.. _pygad-2110:
PyGAD 2.11.0
------------
Release Date: 16 February 2021
1. In the ``gene_space`` argument, the user can use a dictionary to
specify the lower and upper limits of the gene. This dictionary must
have only 2 items with keys ``low`` and ``high`` to specify the low
and high limits of the gene, respectively. This way, PyGAD takes care
of not exceeding the value limits of the gene. For a problem with
only 2 genes, then using
``gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}]``
means the accepted values in the first gene start from 1 (inclusive)
to 5 (exclusive) while the second one has values between 0.2
(inclusive) and 0.85 (exclusive). For more information, please check
the `Limit the Gene Value
Range `__
section of the documentation.
2. The ``plot_result()`` method returns the figure so that the user can
save it.
3. Bug fixes in copying elements from the gene space.
4. For a gene with a set of discrete values (more than 1 value) in the
``gene_space`` parameter like ``[0, 1]``, it was possible that the
gene value may not change after mutation. That is if the current
value is 0, then the randomly selected value could also be 0. Now, it
is verified that the new value is changed. So, if the current value
is 0, then the new value after mutation will not be 0 but 1.
.. _pygad-2120:
PyGAD 2.12.0
------------
Release Date: 20 February 2021
1. 4 new instance attributes are added to hold temporary results after
each generation: ``last_generation_fitness`` holds the fitness values
of the solutions in the last generation, ``last_generation_parents``
holds the parents selected from the last generation,
``last_generation_offspring_crossover`` holds the offspring generated
after applying the crossover in the last generation, and
``last_generation_offspring_mutation`` holds the offspring generated
after applying the mutation in the last generation. You can access
these attributes inside the ``on_generation()`` method for example.
2. A bug fixed when the ``initial_population`` parameter is used. The
bug occurred due to a mismatch between the data type of the array
assigned to ``initial_population`` and the gene type in the
``gene_type`` attribute. Assuming that the array assigned to the
``initial_population`` parameter is
``((1, 1), (3, 3), (5, 5), (7, 7))`` which has type ``int``. When
``gene_type`` is set to ``float``, then the genes will not be float
but casted to ``int`` because the defined array has ``int`` type. The
bug is fixed by forcing the array assigned to ``initial_population``
to have the data type in the ``gene_type`` attribute. Check the
`issue at
GitHub `__:
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/27
Thanks to Andrei Rozanski [PhD Bioinformatics Specialist, Department of
Tissue Dynamics and Regeneration, Max Planck Institute for Biophysical
Chemistry, Germany] for opening my eye to the first change.
Thanks to `Marios
Giouvanakis `__,
a PhD candidate in Electrical & Computer Engineer, `Aristotle University
of Thessaloniki (Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης),
Greece `__, for emailing me about the second
issue.
.. _pygad-2130:
PyGAD 2.13.0
------------
Release Date: 12 March 2021
1. A new ``bool`` parameter called ``allow_duplicate_genes`` is
supported. If ``True``, which is the default, then a
solution/chromosome may have duplicate gene values. If ``False``,
then each gene will have a unique value in its solution. Check the
`Prevent Duplicates in Gene
Values `__
section for more details.
2. The ``last_generation_fitness`` is updated at the end of each
generation not at the beginning. This keeps the fitness values of the
most up-to-date population assigned to the
``last_generation_fitness`` parameter.
.. _pygad-2140:
PyGAD 2.14.0
------------
PyGAD 2.14.0 has an issue that is solved in PyGAD 2.14.1. Please
consider using 2.14.1 not 2.14.0.
Release Date: 19 May 2021
1. `Issue
#40 `__
is solved. Now, the ``None`` value works with the ``crossover_type``
and ``mutation_type`` parameters:
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/40
2. The ``gene_type`` parameter supports accepting a
``list/tuple/numpy.ndarray`` of numeric data types for the genes.
This helps to control the data type of each individual gene.
Previously, the ``gene_type`` can be assigned only to a single data
type that is applied for all genes. For more information, check the
`More about the ``gene_type``
Parameter `__
section. Thanks to `Rainer
Engel `__
for asking about this feature in `this
discussion `__:
https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43
3. A new ``bool`` attribute named ``gene_type_single`` is added to the
``pygad.GA`` class. It is ``True`` when there is a single data type
assigned to the ``gene_type`` parameter. When the ``gene_type``
parameter is assigned a ``list/tuple/numpy.ndarray``, then
``gene_type_single`` is set to ``False``.
4. The ``mutation_by_replacement`` flag now has no effect if
``gene_space`` exists except for the genes with ``None`` values. For
example, for ``gene_space=[None, [5, 6]]`` the
``mutation_by_replacement`` flag affects only the first gene which
has ``None`` for its value space.
5. When an element has a value of ``None`` in the ``gene_space``
parameter (e.g. ``gene_space=[None, [5, 6]]``), then its value will
be randomly generated for each solution rather than being generate
once for all solutions. Previously, the gene with ``None`` value in
``gene_space`` is the same across all solutions
6. Some changes in the documentation according to `issue
#32 `__:
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/32
.. _pygad-2142:
PyGAD 2.14.2
------------
Release Date: 27 May 2021
1. Some bug fixes when the ``gene_type`` parameter is nested. Thanks to
`Rainer
Engel `__
for opening `a
discussion `__
to report this bug:
https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763342
`Rainer
Engel `__
helped a lot in suggesting new features and suggesting enhancements in
2.14.0 to 2.14.2 releases.
.. _pygad-2143:
PyGAD 2.14.3
------------
Release Date: 6 June 2021
1. Some bug fixes when setting the ``save_best_solutions`` parameter to
``True``. Previously, the best solution for generation ``i`` was
added into the ``best_solutions`` attribute at generation ``i+1``.
Now, the ``best_solutions`` attribute is updated by each best
solution at its exact generation.
.. _pygad-2150:
PyGAD 2.15.0
------------
Release Date: 17 June 2021
1. Control the precision of all genes/individual genes. Thanks to
`Rainer `__ for asking about this
feature:
https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/43#discussioncomment-763452
2. A new attribute named ``last_generation_parents_indices`` holds the
indices of the selected parents in the last generation.
3. In adaptive mutation, no need to recalculate the fitness values of
the parents selected in the last generation as these values can be
returned based on the ``last_generation_fitness`` and
``last_generation_parents_indices`` attributes. This speeds-up the
adaptive mutation.
4. When a sublist has a value of ``None`` in the ``gene_space``
parameter (e.g. ``gene_space=[[1, 2, 3], [5, 6, None]]``), then its
value will be randomly generated for each solution rather than being
generated once for all solutions. Previously, a value of ``None`` in
a sublist of the ``gene_space`` parameter was identical across all
solutions.
5. The dictionary assigned to the ``gene_space`` parameter itself or
one of its elements has a new key called ``"step"`` to specify the
step of moving from the start to the end of the range specified by
the 2 existing keys ``"low"`` and ``"high"``. An example is
``{"low": 0, "high": 30, "step": 2}`` to have only even values for
the gene(s) starting from 0 to 30. For more information, check the
`More about the ``gene_space``
Parameter `__
section.
https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/48
6. A new function called ``predict()`` is added in both the
``pygad.kerasga`` and ``pygad.torchga`` modules to make predictions.
This makes it easier than using custom code each time a prediction
is to be made.
7. A new parameter called ``stop_criteria`` allows the user to specify
one or more stop criteria to stop the evolution based on some
conditions. Each criterion is passed as ``str`` which has a stop
word. The current 2 supported words are ``reach`` and ``saturate``.
``reach`` stops the ``run()`` method if the fitness value is equal
to or greater than a given fitness value. An example for ``reach``
is ``"reach_40"`` which stops the evolution if the fitness is >= 40.
``saturate`` means stop the evolution if the fitness saturates for a
given number of consecutive generations. An example for ``saturate``
is ``"saturate_7"`` which means stop the ``run()`` method if the
fitness does not change for 7 consecutive generations. Thanks to
`Rainer `__ for asking about this
feature:
https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/44
8. A new bool parameter, defaults to ``False``, named
``save_solutions`` is added to the constructor of the ``pygad.GA``
class. If ``True``, then all solutions in each generation are
appended into an attribute called ``solutions`` which is NumPy
array.
9. The ``plot_result()`` method is renamed to ``plot_fitness()``. The
users should migrate to the new name as the old name will be removed
in the future.
10. Four new optional parameters are added to the ``plot_fitness()``
function in the ``pygad.GA`` class which are ``font_size=14``,
``save_dir=None``, ``color="#3870FF"``, and ``plot_type="plot"``.
Use ``font_size`` to change the font of the plot title and labels.
``save_dir`` accepts the directory to which the figure is saved. It
defaults to ``None`` which means do not save the figure. ``color``
changes the color of the plot. ``plot_type`` changes the plot type
which can be either ``"plot"`` (default), ``"scatter"``, or
``"bar"``.
https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/47
11. The default value of the ``title`` parameter in the
``plot_fitness()`` method is ``"PyGAD - Generation vs. Fitness"``
rather than ``"PyGAD - Iteration vs. Fitness"``.
12. A new method named ``plot_new_solution_rate()`` creates, shows, and
returns a figure showing the rate of new/unique solutions explored
in each generation. It accepts the same parameters as in the
``plot_fitness()`` method. This method only works when
``save_solutions=True`` in the ``pygad.GA`` class's constructor.
13. A new method named ``plot_genes()`` creates, shows, and returns a
figure to show how each gene changes per each generation. It accepts
similar parameters like the ``plot_fitness()`` method in addition to
the ``graph_type``, ``fill_color``, and ``solutions`` parameters.
The ``graph_type`` parameter can be either ``"plot"`` (default),
``"boxplot"``, or ``"histogram"``. ``fill_color`` accepts the fill
color which works when ``graph_type`` is either ``"boxplot"`` or
``"histogram"``. ``solutions`` can be either ``"all"`` or ``"best"``
to decide whether all solutions or only best solutions are used.
14. The ``gene_type`` parameter now supports controlling the precision
of ``float`` data types. For a gene, rather than assigning just the
data type like ``float``, assign a
``list``/``tuple``/``numpy.ndarray`` with 2 elements where the first
one is the type and the second one is the precision. For example,
``[float, 2]`` forces a gene with a value like ``0.1234`` to be
``0.12``. For more information, check the `More about the
``gene_type``
Parameter `__
section.
.. _pygad-2151:
PyGAD 2.15.1
------------
Release Date: 18 June 2021
1. Fix a bug when ``keep_parents`` is set to a positive integer.
https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/49
PyGAD Projects at GitHub
========================
The PyGAD library is available at PyPI at this page
https://pypi.org/project/pygad. PyGAD is built out of a number of
open-source GitHub projects. A brief note about these projects is given
in the next subsections.
`GeneticAlgorithmPython `__
--------------------------------------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/GeneticAlgorithmPython
`GeneticAlgorithmPython `__
is the first project which is an open-source Python 3 project for
implementing the genetic algorithm based on NumPy.
`NumPyANN `__
----------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/NumPyANN
`NumPyANN `__ builds artificial
neural networks in **Python 3** using **NumPy** from scratch. The
purpose of this project is to only implement the **forward pass** of a
neural network without using a training algorithm. Currently, it only
supports classification and later regression will be also supported.
Moreover, only one class is supported per sample.
`NeuralGenetic `__
--------------------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/NeuralGenetic
`NeuralGenetic `__ trains
neural networks using the genetic algorithm based on the previous 2
projects
`GeneticAlgorithmPython `__
and `NumPyANN `__.
`NumPyCNN `__
----------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/NumPyCNN
`NumPyCNN `__ builds
convolutional neural networks using NumPy. The purpose of this project
is to only implement the **forward pass** of a convolutional neural
network without using a training algorithm.
`CNNGenetic `__
--------------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/CNNGenetic
`CNNGenetic `__ trains
convolutional neural networks using the genetic algorithm. It uses the
`GeneticAlgorithmPython `__
project for building the genetic algorithm.
`KerasGA `__
--------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/KerasGA
`KerasGA `__ trains
`Keras `__ models using the genetic algorithm. It uses
the
`GeneticAlgorithmPython `__
project for building the genetic algorithm.
`TorchGA `__
--------------------------------------------------
GitHub Link: https://github.com/ahmedfgad/TorchGA
`TorchGA `__ trains
`PyTorch `__ models using the genetic algorithm. It
uses the
`GeneticAlgorithmPython `__
project for building the genetic algorithm.
`pygad.torchga `__:
https://github.com/ahmedfgad/TorchGA
Stackoverflow Questions about PyGAD
===================================
.. _how-do-i-proceed-to-load-a-gainstance-as-pkl-format-in-pygad:
`How do I proceed to load a ga_instance as “.pkl” format in PyGad? `__
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
`Binary Classification NN Model Weights not being Trained in PyGAD `__
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
`How to solve TSP problem using pyGAD package? `__
---------------------------------------------------------------------------------------------------------------------------------------------
`How can I save a matplotlib plot that is the output of a function in jupyter? `__
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
`How do I query the best solution of a pyGAD GA instance? `__
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
`Multi-Input Multi-Output in Genetic algorithm (python) `__
--------------------------------------------------------------------------------------------------------------------------------------------------------------
Submitting Issues
=================
If there is an issue using PyGAD, then use any of your preferred option
to discuss that issue.
One way is `submitting an
issue `__
into this GitHub project
(`github.com/ahmedfgad/GeneticAlgorithmPython `__)
in case something is not working properly or to ask for questions.
If this is not a proper option for you, then check the `Contact
Us `__
section for more contact details.
Ask for Feature
===============
PyGAD is actively developed with the goal of building a dynamic library
for suporting a wide-range of problems to be optimized using the genetic
algorithm.
To ask for a new feature, either `submit an
issue `__
into this GitHub project
(`github.com/ahmedfgad/GeneticAlgorithmPython `__)
or send an e-mail to ahmed.f.gad@gmail.com.
Also check the `Contact
Us `__
section for more contact details.
Projects Built using PyGAD
==========================
If you created a project that uses PyGAD, then we can support you by
mentioning this project here in PyGAD's documentation.
To do that, please send a message at ahmed.f.gad@gmail.com or check the
`Contact
Us `__
section for more contact details.
Within your message, please send the following details:
- Project title
- Brief description
- Preferably, a link that directs the readers to your project
Tutorials about PyGAD
=====================
`Adaptive Mutation in Genetic Algorithm with Python Examples `__
-----------------------------------------------------------------------------------------------------------------------------------------------------
In this tutorial, we’ll see why mutation with a fixed number of genes is
bad, and how to replace it with adaptive mutation. Using the `PyGAD
Python 3 library `__, we’ll discuss a few
examples that use both random and adaptive mutation.
`Clustering Using the Genetic Algorithm in Python `__
-------------------------------------------------------------------------------------------------------------------------
This tutorial discusses how the genetic algorithm is used to cluster
data, starting from random clusters and running until the optimal
clusters are found. We'll start by briefly revising the K-means
clustering algorithm to point out its weak points, which are later
solved by the genetic algorithm. The code examples in this tutorial are
implemented in Python using the `PyGAD
library `__.
`Working with Different Genetic Algorithm Representations in Python `__
--------------------------------------------------------------------------------------------------------------------------------------------------------------------
Depending on the nature of the problem being optimized, the genetic
algorithm (GA) supports two different gene representations: binary, and
decimal. The binary GA has only two values for its genes, which are 0
and 1. This is easier to manage as its gene values are limited compared
to the decimal GA, for which we can use different formats like float or
integer, and limited or unlimited ranges.
This tutorial discusses how the
`PyGAD `__ library supports the two GA
representations, binary and decimal.
.. _5-genetic-algorithm-applications-using-pygad:
`5 Genetic Algorithm Applications Using PyGAD `__
-------------------------------------------------------------------------------------------------------------------------
This tutorial introduces PyGAD, an open-source Python library for
implementing the genetic algorithm and training machine learning
algorithms. PyGAD supports 19 parameters for customizing the genetic
algorithm for various applications.
Within this tutorial we'll discuss 5 different applications of the
genetic algorithm and build them using PyGAD.
`Train Neural Networks Using a Genetic Algorithm in Python with PyGAD `__
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
The genetic algorithm (GA) is a biologically-inspired optimization
algorithm. It has in recent years gained importance, as it’s simple
while also solving complex problems like travel route optimization,
training machine learning algorithms, working with single and
multi-objective problems, game playing, and more.
Deep neural networks are inspired by the idea of how the biological
brain works. It’s a universal function approximator, which is capable of
simulating any function, and is now used to solve the most complex
problems in machine learning. What’s more, they’re able to work with all
types of data (images, audio, video, and text).
Both genetic algorithms (GAs) and neural networks (NNs) are similar, as
both are biologically-inspired techniques. This similarity motivates us
to create a hybrid of both to see whether a GA can train NNs with high
accuracy.
This tutorial uses `PyGAD `__, a Python
library that supports building and training NNs using a GA.
`PyGAD `__ offers both classification and
regression NNs.
`Building a Game-Playing Agent for CoinTex Using the Genetic Algorithm `__
----------------------------------------------------------------------------------------------------------------------------------------------------------
In this tutorial we'll see how to build a game-playing agent using only
the genetic algorithm to play a game called
`CoinTex `__,
which is developed in the Kivy Python framework. The objective of
CoinTex is to collect the randomly distributed coins while avoiding
collision with fire and monsters (that move randomly). The source code
of CoinTex can be found `on
GitHub `__.
The genetic algorithm is the only AI used here; there is no other
machine/deep learning model used with it. We'll implement the genetic
algorithm using
`PyGad `__.
This tutorial starts with a quick overview of CoinTex followed by a
brief explanation of the genetic algorithm, and how it can be used to
create the playing agent. Finally, we'll see how to implement these
ideas in Python.
The source code of the genetic algorithm agent is available
`here `__,
and you can download the code used in this tutorial from
`here `__.
`How To Train Keras Models Using the Genetic Algorithm with PyGAD `__
--------------------------------------------------------------------------------------------------------------------------------------------------------
PyGAD is an open-source Python library for building the genetic
algorithm and training machine learning algorithms. It offers a wide
range of parameters to customize the genetic algorithm to work with
different types of problems.
PyGAD has its own modules that support building and training neural
networks (NNs) and convolutional neural networks (CNNs). Despite these
modules working well, they are implemented in Python without any
additional optimization measures. This leads to comparatively high
computational times for even simple problems.
The latest PyGAD version, 2.8.0 (released on 20 September 2020),
supports a new module to train Keras models. Even though Keras is built
in Python, it's fast. The reason is that Keras uses TensorFlow as a
backend, and TensorFlow is highly optimized.
This tutorial discusses how to train Keras models using PyGAD. The
discussion includes building Keras models using either the Sequential
Model or the Functional API, building an initial population of Keras
model parameters, creating an appropriate fitness function, and more.
|image1|
`Train PyTorch Models Using Genetic Algorithm with PyGAD `__
---------------------------------------------------------------------------------------------------------------------------------------------
`PyGAD `__ is a genetic algorithm Python
3 library for solving optimization problems. One of these problems is
training machine learning algorithms.
PyGAD has a module called
`pygad.kerasga `__. It trains
Keras models using the genetic algorithm. On January 3rd, 2021, a new
release of `PyGAD 2.10.0 `__ brought a
new module called
`pygad.torchga `__ to train
PyTorch models. It’s very easy to use, but there are a few tricky steps.
So, in this tutorial, we’ll explore how to use PyGAD to train PyTorch
models.
|image2|
`A Guide to Genetic ‘Learning’ Algorithms for Optimization `__
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
PyGAD in Other Languages
========================
French
------
`Cómo los algoritmos genéticos pueden competir con el descenso de
gradiente y el
backprop `__
Bien que la manière standard d'entraîner les réseaux de neurones soit la
descente de gradient et la rétropropagation, il y a d'autres joueurs
dans le jeu. L'un d'eux est les algorithmes évolutionnaires, tels que
les algorithmes génétiques.
Utiliser un algorithme génétique pour former un réseau de neurones
simple pour résoudre le OpenAI CartPole Jeu. Dans cet article, nous
allons former un simple réseau de neurones pour résoudre le OpenAI
CartPole . J'utiliserai PyTorch et PyGAD .
|image3|
Spanish
-------
`Cómo los algoritmos genéticos pueden competir con el descenso de
gradiente y el
backprop `__
Aunque la forma estandar de entrenar redes neuronales es el descenso de
gradiente y la retropropagacion, hay otros jugadores en el juego, uno de
ellos son los algoritmos evolutivos, como los algoritmos geneticos.
Usa un algoritmo genetico para entrenar una red neuronal simple para
resolver el Juego OpenAI CartPole. En este articulo, entrenaremos una
red neuronal simple para resolver el OpenAI CartPole . Usare PyTorch y
PyGAD .
|image4|
Korean
------
`[PyGAD] Python 에서 Genetic Algorithm 을 사용해보기 `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|image5|
파이썬에서 genetic algorithm을 사용하는 패키지들을 다 사용해보진
않았지만, 확장성이 있어보이고, 시도할 일이 있어서 살펴봤다.
이 패키지에서 가장 인상 깊었던 것은 neural network에서 hyper parameter
탐색을 gradient descent 방식이 아닌 GA로도 할 수 있다는 것이다.
개인적으로 이 부분이 어느정도 초기치를 잘 잡아줄 수 있는 역할로도 쓸 수
있고, Loss가 gradient descent 하기 어려운 구조에서 대안으로 쓸 수 있을
것으로도 생각된다.
일단 큰 흐름은 다음과 같이 된다.
사실 완전히 흐름이나 각 parameter에 대한 이해는 부족한 상황
Turkish
-------
`PyGAD ile Genetik Algoritmayı Kullanarak Keras Modelleri Nasıl Eğitilir `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This is a translation of an original English tutorial published at
Paperspace: `How To Train Keras Models Using the Genetic Algorithm with
PyGAD `__
PyGAD, genetik algoritma oluşturmak ve makine öğrenimi algoritmalarını
eğitmek için kullanılan açık kaynaklı bir Python kitaplığıdır. Genetik
algoritmayı farklı problem türleri ile çalışacak şekilde özelleştirmek
için çok çeşitli parametreler sunar.
PyGAD, sinir ağları (NN’ler) ve evrişimli sinir ağları (CNN’ler)
oluşturmayı ve eğitmeyi destekleyen kendi modüllerine sahiptir. Bu
modüllerin iyi çalışmasına rağmen, herhangi bir ek optimizasyon önlemi
olmaksızın Python’da uygulanırlar. Bu, basit problemler için bile
nispeten yüksek hesaplama sürelerine yol açar.
En son PyGAD sürümü 2.8.0 (20 Eylül 2020'de piyasaya sürüldü), Keras
modellerini eğitmek için yeni bir modülü destekliyor. Keras Python’da
oluşturulmuş olsa da hızlıdır. Bunun nedeni, Keras’ın arka uç olarak
TensorFlow kullanması ve TensorFlow’un oldukça optimize edilmiş
olmasıdır.
Bu öğreticide, PyGAD kullanılarak Keras modellerinin nasıl eğitileceği
anlatılmaktadır. Tartışma, Sıralı Modeli veya İşlevsel API’yi kullanarak
Keras modellerini oluşturmayı, Keras model parametrelerinin ilk
popülasyonunu oluşturmayı, uygun bir uygunluk işlevi oluşturmayı ve daha
fazlasını içerir.
|image6|
Hungarian
---------
.. _tensorflow-alapozó-10-neurális-hálózatok-tenyésztése-genetikus-algoritmussal-pygad-és-openai-gym-használatával:
`Tensorflow alapozó 10. Neurális hálózatok tenyésztése genetikus algoritmussal PyGAD és OpenAI Gym használatával `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Hogy kontextusba helyezzem a genetikus algoritmusokat, ismételjük kicsit
át, hogy hogyan működik a gradient descent és a backpropagation, ami a
neurális hálók tanításának általános módszere. Az erről írt cikkemet itt
tudjátok elolvasni.
A hálózatok tenyésztéséhez a
`PyGAD `__ nevű
programkönyvtárat használjuk, így mindenek előtt ezt kell telepítenünk,
valamint a Tensorflow-t és a Gym-et, amit Colabban már eleve telepítve
kapunk.
Maga a PyGAD egy teljesen általános genetikus algoritmusok futtatására
képes rendszer. Ennek a kiterjesztése a KerasGA, ami az általános motor
Tensorflow (Keras) neurális hálókon történő futtatását segíti. A 47.
sorban létrehozott KerasGA objektum ennek a kiterjesztésnek a része és
arra szolgál, hogy a paraméterként átadott modellből a második
paraméterben megadott számosságú populációt hozzon létre. Mivel a
hálózatunk 386 állítható paraméterrel rendelkezik, ezért a DNS-ünk itt
386 elemből fog állni. A populáció mérete 10 egyed, így a kezdő
populációnk egy 10x386 elemű mátrix lesz. Ezt adjuk át az 51. sorban az
initial_population paraméterben.
|image7|
Russian
-------
`PyGAD: библиотека для имплементации генетического алгоритма `__
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PyGAD — это библиотека для имплементации генетического алгоритма. Кроме
того, библиотека предоставляет доступ к оптимизированным реализациям
алгоритмов машинного обучения. PyGAD разрабатывали на Python 3.
Библиотека PyGAD поддерживает разные типы скрещивания, мутации и
селекции родителя. PyGAD позволяет оптимизировать проблемы с помощью
генетического алгоритма через кастомизацию целевой функции.
Кроме генетического алгоритма, библиотека содержит оптимизированные
имплементации алгоритмов машинного обучения. На текущий момент PyGAD
поддерживает создание и обучение нейросетей для задач классификации.
Библиотека находится в стадии активной разработки. Создатели планируют
добавление функционала для решения бинарных задач и имплементации новых
алгоритмов.
PyGAD разрабатывали на Python 3.7.3. Зависимости включают в себя NumPy
для создания и манипуляции массивами и Matplotlib для визуализации. Один
из изкейсов использования инструмента — оптимизация весов, которые
удовлетворяют заданной функции.
|image8|
Papers using PyGAD
==================
A number of research papers used PyGAD and here are some of them:
- Jaros, Marta, and Jiri Jaros. "Performance-Cost Optimization of
Moldable Scientific Workflows."
- Koch, Chris, and Edgar Dobriban. "AttenGen: Generating Live
Attenuated Vaccine Candidates using Machine Learning." (2021).
- Thorat, Divya. *Enhanced genetic algorithm to reduce makespan of
multiple jobs in map-reduce application on serverless platform*.
Diss. Dublin, National College of Ireland, 2020.
- Bhardwaj, Bhavya, et al. "Windfarm optimization using Nelder-Mead and
Particle Swarm optimization." *2021 7th International Conference on
Electrical Energy Systems (ICEES)*. IEEE, 2021.
- Bernardo, Reginald Christian S. and J. Said. “Towards a
model-independent reconstruction approach for late-time Hubble data.”
(2021).
- Duong, Tri Dung, Qian Li, and Guandong Xu. "Prototype-based
Counterfactual Explanation for Causal Classification." *arXiv
preprint arXiv:2105.00703* (2021).
For More Information
====================
There are different resources that can be used to get started with the
genetic algorithm and building it in Python.
Tutorial: Implementing Genetic Algorithm in Python
--------------------------------------------------
To start with coding the genetic algorithm, you can check the tutorial
titled `Genetic Algorithm Implementation in
Python `__
available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
`This
tutorial `__
is prepared based on a previous version of the project but it still a
good resource to start with coding the genetic algorithm.
|image9|
Tutorial: Introduction to Genetic Algorithm
-------------------------------------------
Get started with the genetic algorithm by reading the tutorial titled
`Introduction to Optimization with Genetic
Algorithm `__
which is available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
|image10|
Tutorial: Build Neural Networks in Python
-----------------------------------------
Read about building neural networks in Python through the tutorial
titled `Artificial Neural Network Implementation using NumPy and
Classification of the Fruits360 Image
Dataset `__
available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
|image11|
Tutorial: Optimize Neural Networks with Genetic Algorithm
---------------------------------------------------------
Read about training neural networks using the genetic algorithm through
the tutorial titled `Artificial Neural Networks Optimization using
Genetic Algorithm with
Python `__
available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
|image12|
Tutorial: Building CNN in Python
--------------------------------
To start with coding the genetic algorithm, you can check the tutorial
titled `Building Convolutional Neural Network using NumPy from
Scratch `__
available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
- `Chinese Translation `__
`This
tutorial `__)
is prepared based on a previous version of the project but it still a
good resource to start with coding CNNs.
|image13|
Tutorial: Derivation of CNN from FCNN
-------------------------------------
Get started with the genetic algorithm by reading the tutorial titled
`Derivation of Convolutional Neural Network from Fully Connected Network
Step-By-Step `__
which is available at these links:
- `LinkedIn `__
- `Towards Data
Science `__
- `KDnuggets `__
|image14|
Book: Practical Computer Vision Applications Using Deep Learning with CNNs
--------------------------------------------------------------------------
You can also check my book cited as `Ahmed Fawzy Gad 'Practical Computer
Vision Applications Using Deep Learning with CNNs'. Dec. 2018, Apress,
978-1-4842-4167-7 `__
which discusses neural networks, convolutional neural networks, deep
learning, genetic algorithm, and more.
Find the book at these links:
- `Amazon `__
- `Springer `__
- `Apress `__
- `O'Reilly `__
- `Google Books `__
.. figure:: https://user-images.githubusercontent.com/16560492/78830077-ae7c2800-79e7-11ea-980b-53b6bd879eeb.jpg
:alt:
Contact Us
==========
- E-mail: ahmed.f.gad@gmail.com
- `LinkedIn `__
- `Amazon Author Page `__
- `Heartbeat `__
- `Paperspace `__
- `KDnuggets `__
- `TowardsDataScience `__
- `GitHub `__
.. figure:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
:alt:
Thank you for using
`PyGAD `__ :)
.. |image1| image:: https://user-images.githubusercontent.com/16560492/111009628-2b372500-8362-11eb-90cf-01b47d831624.png
:target: https://blog.paperspace.com/train-keras-models-using-genetic-algorithm-with-pygad
.. |image2| image:: https://user-images.githubusercontent.com/16560492/111009678-5457b580-8362-11eb-899a-39e2f96984df.png
:target: https://neptune.ai/blog/train-pytorch-models-using-genetic-algorithm-with-pygad
.. |image3| image:: https://user-images.githubusercontent.com/16560492/111009275-3178d180-8361-11eb-9e86-7fb1519acde7.png
:target: https://www.hebergementwebs.com/nouvelles/comment-les-algorithmes-genetiques-peuvent-rivaliser-avec-la-descente-de-gradient-et-le-backprop
.. |image4| image:: https://user-images.githubusercontent.com/16560492/111009257-232ab580-8361-11eb-99a5-7226efbc3065.png
:target: https://www.hebergementwebs.com/noticias/como-los-algoritmos-geneticos-pueden-competir-con-el-descenso-de-gradiente-y-el-backprop
.. |image5| image:: https://user-images.githubusercontent.com/16560492/108586306-85bd0280-731b-11eb-874c-7ac4ce1326cd.jpg
:target: https://data-newbie.tistory.com/m/685
.. |image6| image:: https://user-images.githubusercontent.com/16560492/108586601-85be0200-731d-11eb-98a4-161c75a1f099.jpg
:target: https://erencan34.medium.com/pygad-ile-genetik-algoritmay%C4%B1-kullanarak-keras-modelleri-nas%C4%B1l-e%C4%9Fitilir-cf92639a478c
.. |image7| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
:target: https://thebojda.medium.com/tensorflow-alapoz%C3%B3-10-24f7767d4a2c
.. |image8| image:: https://user-images.githubusercontent.com/16560492/101267295-c74c0180-375f-11eb-9ad0-f8e37bd796ce.png
:target: https://neurohive.io/ru/frameworki/pygad-biblioteka-dlya-implementacii-geneticheskogo-algoritma
.. |image9| image:: https://user-images.githubusercontent.com/16560492/78830052-a3c19300-79e7-11ea-8b9b-4b343ea4049c.png
:target: https://www.linkedin.com/pulse/genetic-algorithm-implementation-python-ahmed-gad
.. |image10| image:: https://user-images.githubusercontent.com/16560492/82078259-26252d00-96e1-11ea-9a02-52a99e1054b9.jpg
:target: https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
.. |image11| image:: https://user-images.githubusercontent.com/16560492/82078281-30472b80-96e1-11ea-8017-6a1f4383d602.jpg
:target: https://www.linkedin.com/pulse/artificial-neural-network-implementation-using-numpy-fruits360-gad
.. |image12| image:: https://user-images.githubusercontent.com/16560492/82078300-376e3980-96e1-11ea-821c-aa6b8ceb44d4.jpg
:target: https://www.linkedin.com/pulse/artificial-neural-networks-optimization-using-genetic-ahmed-gad
.. |image13| image:: https://user-images.githubusercontent.com/16560492/82431022-6c3a1200-9a8e-11ea-8f1b-b055196d76e3.png
:target: https://www.linkedin.com/pulse/building-convolutional-neural-network-using-numpy-from-ahmed-gad
.. |image14| image:: https://user-images.githubusercontent.com/16560492/82431369-db176b00-9a8e-11ea-99bd-e845192873fc.png
:target: https://www.linkedin.com/pulse/derivation-convolutional-neural-network-from-fully-connected-gad