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PyGAD 3.3.0
Release Date 29 January 2024 1. Solve bugs when multi-objective optimization is used. ahmedfgad#238 2. When the `stop_ciiteria` parameter is used with the `reach` keyword, then multiple numeric values can be passed when solving a multi-objective problem. For example, if a problem has 3 objective functions, then `stop_criteria="reach_10_20_30"` means the GA stops if the fitness of the 3 objectives are at least 10, 20, and 30, respectively. The number values must match the number of objective functions. If a single value found (e.g. `stop_criteria=reach_5`) when solving a multi-objective problem, then it is used across all the objectives. ahmedfgad#238 3. The `delay_after_gen` parameter is now deprecated and will be removed in a future release. If it is necessary to have a time delay after each generation, then assign a callback function/method to the `on_generation` parameter to pause the evolution. 4. Parallel processing now supports calculating the fitness during adaptive mutation. ahmedfgad#201 5. The population size can be changed during runtime by changing all the parameters that would affect the size of any thing used by the GA. For more information, check the [Change Population Size during Runtime](https://pygad.readthedocs.io/en/latest/pygad_more.html#change-population-size-during-runtime) section. ahmedfgad#234 6. When a dictionary exists in the `gene_space` parameter without a step, then mutation occurs by adding a random value to the gene value. The random vaue is generated based on the 2 parameters `random_mutation_min_val` and `random_mutation_max_val`. For more information, check the [How Mutation Works with the gene_space Parameter?](https://pygad.readthedocs.io/en/latest/pygad_more.html#how-mutation-works-with-the-gene-space-parameter) section. ahmedfgad#229 7. Add `object` as a supported data type for int (GA.supported_int_types) and float (GA.supported_float_types). ahmedfgad#174 8. Use the `raise` clause instead of the `sys.exit(-1)` to terminate the execution. ahmedfgad#213 9. Fix a bug when multi-objective optimization is used with batch fitness calculation (e.g. `fitness_batch_size` set to a non-zero number). 10. 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.
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‎docs/source/conf.py

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# -- Project information -----------------------------------------------------
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project = 'PyGAD'
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copyright = '2023, Ahmed Fawzy Gad'
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copyright = '2024, Ahmed Fawzy Gad'
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author = 'Ahmed Fawzy Gad'
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# The full version, including alpha/beta/rc tags
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release = '3.2.0'
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release = '3.3.0'
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master_doc = 'index'
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‎docs/source/pygad_more.rst

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How Mutation Works with the ``gene_space`` Parameter?
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-----------------------------------------------------
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If a gene has its static space defined in the ``gene_space`` parameter,
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then mutation works by replacing the gene value by a value randomly
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selected from the gene space. This happens for both ``int`` and
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``float`` data types.
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Mutation changes based on whether the ``gene_space`` has a continuous
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range or discrete set of values.
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If a gene has its **static/discrete space** defined in the
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``gene_space`` parameter, then mutation works by replacing the gene
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value by a value randomly selected from the gene space. This happens for
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both ``int`` and ``float`` data types.
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For example, the following ``gene_space`` has the static space
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``[1, 2, 3]`` defined for the first gene. So, th 579F is gene can only have a
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value is 4.5. If the gene type is integer, then the value will be
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rounded.
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On the other hand, if a gene has a **continuous space** defined in the
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``gene_space`` parameter, then mutation occurs by adding a random value
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to the current gene value.
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For example, the following ``gene_space`` has the continuous space
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defined by the dictionary ``{'low': 1, 'high': 5}``. This applies to all
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genes. So, mutation is applied to one or more selected genes by adding a
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random value to the current gene value.
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.. code:: python
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Gene space: {'low': 1, 'high': 5}
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Solution: [1.5, 3.4]
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Assuming ``random_mutation_min_val=-1`` and
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``random_mutation_max_val=1``, then a random value such as ``0.3`` can
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be added to the gene(s) participating in mutation. If only the first
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gene is mutated, then its new value changes from ``1.5`` to
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``1.5+0.3=1.8``. Note that PyGAD verifies that the new value is within
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the range. In the worst scenarios, the value will be set to either
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boundary of the continuous range. For example, if the gene value is 1.5
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and the random value is -0.55, then the new value is 0.95 which smaller
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than the lower boundary 1. Thus, the gene value will be rounded to 1.
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If the dictionary has a step like the example below, then it is
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considered a discrete range and mutation occurs by randomly selecting a
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value from the set of values. In other words, no random value is added
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to the gene value.
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.. code:: python
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Gene space: {'low': 1, 'high': 5, 'step': 0.5}
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Stop at Any Generation
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======================
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attributes (``self.solutions`` and ``self.solutions_fitness``) only work
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if the ``save_solutions`` parameter is ``True``.
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Change Population Size during Runtime
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=====================================
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Starting from `PyGAD
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3.3.0 <https://pygad.readthedocs.io/en/latest/releases.html#pygad-3-3-0>`__,
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the population size can changed during runtime. In other words, the
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number of solutions/chromosomes and number of genes can be changed.
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The user has to carefully arrange the list of *parameters* and *instance
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attributes* that have to be changed to keep the GA consistent before and
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after changing the population size. Generally, change everything that
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would be used during the GA evolution.
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CAUTION: If the user failed to change a parameter or an instance
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attributes necessary to keep the GA running after the population size
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changed, errors will arise.
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These are examples of the parameters that the user should decide whether
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to change. The user should check the `list of
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parameters <https://pygad.readthedocs.io/en/latest/pygad.html#init>`__
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and decide what to change.
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1. ``population``: The population. It *must* be changed.
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2. ``num_offspring``: The number of offspring to produce out of the
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crossover and mutation operations. Change this parameter if the
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number of offspring have to be changed to be consistent with the new
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population size.
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3. ``num_parents_mating``: The number of solutions to select as parents.
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Change this parameter if the number of parents have to be changed to
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be consistent with the new population size.
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4. ``fitness_func``: If the way of calculating the fitness changes after
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the new population size, then the fitness function have to be
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changed.
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5. ``sol_per_pop``: The number of solutions per population. It is not
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critical to change it but it is recommended to keep this number
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consistent with the number of solutions in the ``population``
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parameter.
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These are examples of the instance attributes that might be changed. The
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user should check the `list of instance
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attributes <https://pygad.readthedocs.io/en/latest/pygad.html#other-instance-attributes-methods>`__
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and decide what to change.
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1. All the ``last_generation_*`` parameters
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1. ``last_generation_fitness``: A 1D NumPy array of fitness values of
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the population.
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2. ``last_generation_parents`` and
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``last_generation_parents_indices``: Two NumPy arrays: 2D array
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representing the parents and 1D array of the parents indices.
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3. ``last_generation_elitism`` and
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``last_generation_elitism_indices``: Must be changed if
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``keep_elitism != 0``. The default value of ``keep_elitism`` is 1.
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Two NumPy arrays: 2D array representing the elitism and 1D array
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of the elitism indices.
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2. ``pop_size``: The population size.
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Prevent Duplicates in Gene Values
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=================================
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‎docs/source/releases.rst

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class is removed. Instead, please use the ``plot_fitness()`` if you
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did not upgrade yet.
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.. _pygad-321:
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.. _pygad-330:
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PyGAD 3.2.1
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PyGAD 3.3.0
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-----------
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Release Date ... 2023
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Release Date 29 January 2024
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1. Fix a bug when multi-objective optimization is used with batch
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fitness calculation (e.g. ``fitness_batch_size`` set to a non-zero
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number).
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1. Solve bugs when multi-objective optimization is used.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/238
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2. Fix a bug in the ``pygad.py`` script when finding the index of the
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best solution. It does not work properly with multi-objective
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optimization where ``self.best_solutions_fitness`` have multiple
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columns.
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2. When the ``stop_ciiteria`` parameter is used with the ``reach``
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keyword, then multiple numeric values can be passed when solving a
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multi-objective problem. For example, if a problem has 3 objective
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functions, then ``stop_criteria="reach_10_20_30"`` means the GA
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stops if the fitness of the 3 objectives are at least 10, 20, and
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30, respectively. The number values must match the number of
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objective functions. If a single value found (e.g.
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``stop_criteria=reach_5``) when solving a multi-objective problem,
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then it is used across all the objectives.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/238
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.. code:: python
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3. The ``delay_after_gen`` parameter is now deprecated and will be
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removed in a future release. If it is necessary to have a time delay
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after each generation, then assign a callback function/method to the
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``on_generation`` parameter to pause the evolution.
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self.best_solution_generation = numpy.where(numpy.array(
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self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0]
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4. Parallel processing now supports calculating the fitness during
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adaptive mutation.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/201
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5. The population size can be changed during runtime by changing all
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the parameters that would affect the size of any thing used by the
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GA. For more information, check the `Change Population Size during
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Runtime <https://pygad.readthedocs.io/en/latest/pygad_more.html#change-population-size-during-runtime>`__
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section.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/234
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6. When a dictionary exists in the ``gene_space`` parameter without a
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step, then mutation occurs by adding a random value to the gene
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value. The random vaue is generated based on the 2 parameters
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``random_mutation_min_val`` and ``random_mutation_max_val``. For
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more information, check the `How Mutation Works with the gene_space
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Parameter? <https://pygad.readthedocs.io/en/latest/pygad_more.html#how-mutation-works-with-the-gene-space-parameter>`__
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section.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/229
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7. Add ``object`` as a supported data type for int
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(GA.supported_int_types) and float (GA.supported_float_types).
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/174
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8. Use the ``raise`` clause instead of the ``sys.exit(-1)`` to
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terminate the execution.
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https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/213
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9. Fix a bug when multi-objective optimization is used with batch
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fitness calculation (e.g. ``fitness_batch_size`` set to a non-zero
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number).
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10. Fix a bug in the ``pygad.py`` script when finding the index of the
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best solution. It does not work properly with multi-objective
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optimization where ``self.best_solutions_fitness`` have multiple
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columns.
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.. code:: python
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self.best_solution_generation = numpy.where(numpy.array(
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self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0]
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PyGAD Projects at GitHub
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========================

‎examples/example_dynamic_population_size.py

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"""
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This is an example to dynamically change the population size (i.e. number of solutions/chromosomes per population) during runtime.
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The following 2 instance attributes must be changed to meet the new desired population size:
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1) population: This is a NumPy array holding the population.
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2) num_offspring: This represents the number of offspring to produce during crossover.
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For example, if the population initially has 20 solutions and 6 genes. To change it to have 30 solutions, then:
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1)population: Create a new NumPy array with the desired size (30, 6) and assign it to the population instance attribute.
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2)num_offspring: Set the num_offspring attribute accordingly (e.g. 29 assuming that keep_elitism has the default value of 1).
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The user has to carefully inspect the parameters and instance attributes to select those that must be changed to be consistent with the new population size.
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Check this link for more information: https://pygad.readthedocs.io/en/latest/pygad_more.html#change-population-size-during-runtime
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"""
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def update_GA(ga_i,
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pop_size):
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"""
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Update the parameters and instance attributes to match the new population size.
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Parameters
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----------
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ga_i : TYPE
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The pygad.GA instance.
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pop_size : TYPE
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The new population size.
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Returns
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-------
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None.
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"""
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ga_i.pop_size = pop_size
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ga_i.sol_per_pop = ga_i.pop_size[0]
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ga_i.num_parents_mating = int(ga_i.pop_size[0]/2)
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# Calculate the new value for the num_offspring parameter.
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if ga_i.keep_elitism != 0:
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ga_i.num_offspring = ga_i.sol_per_pop - ga_i.keep_elitism
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elif ga_i.keep_parents != 0:
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if ga_i.keep_parents == -1:
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ga_i.num_offspring = ga_i.sol_per_pop - ga_i.num_parents_mating
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else:
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ga_i.num_offspring = ga_i.sol_per_pop - ga_i.keep_parents
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ga_i.num_genes = ga_i.pop_size[1]
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ga_i.population = numpy.random.uniform(low=ga_i.init_range_low,
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high=ga_i.init_range_low,
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size=ga_i.pop_size)
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fitness = []
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for solution, solution_idx in enumerate(ga_i.population):
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fitness.append(fitness_func(ga_i, solution, solution_idx))
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ga_i.last_generation_fitness = numpy.array(fitness)
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parents, parents_fitness = ga_i.steady_state_selection(ga_i.last_generation_fitness,
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ga_i.num_parents_mating)
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ga_i.last_generation_elitism = parents[:ga_i.keep_elitism]
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ga_i.last_generation_elitism_indices = parents_fitness[:ga_i.keep_elitism]
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ga_i.last_generation_parents = parents
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ga_i.last_generation_parents_indices = parents_fitness
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def fitness_func(ga_instance, solution, solution_idx):
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return [numpy.random.rand(), numpy.random.rand()]
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return numpy.sum(solution)
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def on_generation(ga_i):
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# The population starts with 20 solutions.
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print(ga_i.generations_completed, ga_i.num_offspring, ga_i.population.shape)
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# At generation 15, increase the population size to 40 solutions.
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print(ga_i.generations_completed, ga_i.population.shape)
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# At generation 15, set the population size to 30 solutions and 10 genes.
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if ga_i.generations_completed >= 15:
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ga_i.num_offspring = 49
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new_population = numpy.zeros(shape=(ga_i.num_offspring+1, ga_i.population.shape[1]), dtype=ga_i.population.dtype)
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new_population[:ga_i.population.shape[0], :] = ga_i.population
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ga_i.population = new_population
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ga_i.pop_size = (30, 10)
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update_GA(ga_i=ga_i,
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pop_size=(30, 10))
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# At generation 10, set the population size to 15 solutions and 8 genes.
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elif ga_i.generations_completed >= 10:
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ga_i.num_offspring = 39
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new_population = numpy.zeros(shape=(ga_i.num_offspring+1, ga_i.population.shape[1]), dtype=ga_i.population.dtype)
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new_population[:ga_i.population.shape[0], :] = ga_i.population
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ga_i.population = new_population
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# At generation 10, increase the population size to 30 solutions.
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update_GA(ga_i=ga_i,
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pop_size=(15, 8))
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# At generation 5, set the population size to 10 solutions and 3 genes.
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elif ga_i.generations_completed >= 5:
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ga_i.num_offspring = 29
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new_population = numpy.zeros(shape=(ga_i.num_offspring+1, ga_i.population.shape[1]), dtype=ga_i.population.dtype)
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new_population[:ga_i.population.shape[0], :] = ga_i.population
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ga_i.population = new_population
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update_GA(ga_i=ga_i,
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pop_size=(10, 3))
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ga_instance = pygad.GA(num_generations=20,
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sol_per_pop=20,
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num_genes=6,
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num_parents_mating=10,
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fitness_func=fitness_func,
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on_generation=on_generation,
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parent_selection_type='nsga2')
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on_generation=on_generation)
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ga_instance.run()
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‎pyproject.toml

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[project]
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name = "pygad"
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version = "3.2.0"
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version = "3.3.0"
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description = "PyGAD: A Python Library for Building the Genetic Algorithm and Training Machine Learning Algoithms (Keras & PyTorch)."
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readme = {file = "README.md", content-type = "text/markdown"}
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requires-python = ">=3"

‎setup.py

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setuptools.setup(
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name="pygad",
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version="3.2.0",
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version="3.3.0",
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author="Ahmed Fawzy Gad",
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install_requires=["numpy", "matplotlib", "cloudpickle",],
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author_email="ahmed.f.gad@gmail.com",

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