Release notes from GeneticAlgorithmPython

Documentation of PyGAD 2.14.3

2021-06-06T06:21:19Z

The new release has 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 solution at its exact generation.

2.14.2: New bade added

2021-05-27T23:51:39Z

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2.14.1: PyGAD 2.14.0 Documentation

2021-05-19T07:28:03Z

Issue ahmedfgad#40 is solved. Now, the None value works with the crossover_type and mutation_type parameters: ahmedfgad#40
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.
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.
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.
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
Some changes in the documentation according to issue ahmedfgad#32: ahmedfgad#32

2.13.0

2021-03-13T00:01:13Z

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2.12.0

2021-02-20T06:48:14Z

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2.11.0

2021-02-16T06:53:36Z

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PyGAD 2.10.2

2021-01-15T16:18:02Z

A bug fix when save_best_solutions=True. Refer to this issue for more information: ahmedfgad#25

PyGAD 2.10.1 Documentation

2021-01-11T02:46:01Z

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.
All the numbers passed to the gene_space parameter are casted to the type specified in the gene_type parameter.
The numpy.uint data type is supported for the parameters that accept integer values.
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.

2.10.0: Link to TorchGA project at GitHub

2021-01-04T02:09:26Z

Link to TorchGA project at GitHub: https://github.com/ahmedfgad/TorchGA

PyGAD 2.9.0

2020-12-05T23:29:23Z

Changes in PyGAD 2.9.0 (06 December 2020):

The fitness values of the initial population are considered in the best_solutions_fitness attribute.
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.
Scattered crossover is supported. To use it, assign the crossover_type parameter the value "scattered".
NumPy arrays are now supported by the gene_space parameter.
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.