selection.

https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/179

.. _pygad-310:

PyGAD 3.1.0

Release Date 20 June 2023

1. Fix a bug when the initial population has duplciate genes if a

nested gene space is used.

2. The ``gene_space`` parameter can no longer be assigned a tuple.

3. Fix a bug when the ``gene_space`` parameter has a member of type

``tuple``.

4. A new instance attribute called ``gene_space_unpacked`` which has

the unpacked ``gene_space``. It is used to solve duplicates. For

infinite ranges in the ``gene_space``, they are unpacked to a

limited number of values (e.g. 100).

5. Bug fixes when creating the initial population using ``gene_space``

attribute.

6. When a ``dict`` is used with the ``gene_space`` attribute, the new

gene value was calculated by summing 2 values: 1) the value sampled

from the ``dict`` 2) a random value returned from the random

mutation range defined by the 2 parameters

``random_mutation_min_val`` and ``random_mutation_max_val``. This

might cause the gene value to exceed the range limit defined in the

``gene_space``. To respect the ``gene_space`` range, this release

only returns the value from the ``dict`` without summing it to a

random value.

7. Formatting the strings using f-string instead of the ``format()``

method. https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/189

8. In the ``__init__()`` of the ``pygad.GA`` class, the logged error

messages are handled using a ``try-except`` block instead of

repeating the ``logger.error()`` command.

https://github.com/ahmedfgad/GeneticAlgorithmPython/pull/189

9. A new class named ``CustomLogger`` is created in the ``pygad.cnn``

module to create a default logger using the ``logging`` module

assigned to the ``logger`` attribute. This class is extended in all

other classes in the module. The constructors of these classes have

a new parameter named ``logger`` which defaults to ``None``. If no

logger is passed, then the default logger in the ``CustomLogger``

class is used.

10. Except for the ``pygad.nn`` module, the ``print()`` function in all

other modules are replaced by the ``logging`` module to log

messages.

11. The callback functions/methods ``on_fitness()``, ``on_parents()``,

``on_crossover()``, and ``on_mutation()`` can return values. These

returned values override the corresponding properties. The output of

``on_fitness()`` overrides the population fitness. The

``on_parents()`` function/method must return 2 values representing

the parents and their indices. The output of ``on_crossover()``

overrides the crossover offspring. The output of ``on_mutation()``

overrides the mutation offspring.

12. Fix a bug when adaptive mutation is used while

``fitness_batch_size``>1.

https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/195

13. When ``allow_duplicate_genes=False`` and a user-defined

``gene_space`` is used, it sometimes happen that there is no room to

solve the duplicates between the 2 genes by simply replacing the

value of one gene by another gene. This release tries to solve such

duplicates by looking for a third gene that will help in solving the

duplicates. These examples explain how it works. Check `this

section <https://pygad.readthedocs.io/en/latest/pygad.html#prevent-duplicates-in-gene-values>`__

for more information.

14. Use probabilities to select parents using the rank parent selection

method.

https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/205

15. The 2 parameters ``random_mutation_min_val`` and

``random_mutation_max_val`` can accept iterables

(list/tuple/numpy.ndarray) with length equal to the number of genes.

This enables customizing the mutation range for each individual

gene.

https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/198

16. The 2 parameters ``init_range_low`` and ``init_range_high`` can

accept iterables (list/tuple/numpy.ndarray) with length equal to the

number of genes. This enables customizing the initial range for each

individual gene when creating the initial population.

17. The ``data`` parameter in the ``predict()`` function of the

``pygad.kerasga`` module can be assigned a data generator.

https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/115

https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/207

18. The ``predict()`` function of the ``pygad.kerasga`` module accepts 3

optional parameters: 1) ``batch_size=None``, ``verbose=0``, and

``steps=None``. Check documentation of the `Keras

Model.predict() <https://keras.io/api/models/model_training_apis>`__

method for more information.

https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/207

19. The documentation is updated to explain how mutation works when

``gene_space`` is used with ``int`` or ``float`` data types. Check

`this

section <https://pygad.readthedocs.io/en/latest/pygad.html#limit-the-gene-value-range-using-the-gene-space-parameter>`__.

https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/198

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