Tags from GeneticAlgorithmPython

2.13.0

2021-03-13T00:01:13Z

2.12.0

2021-02-20T06:48:14Z

2.11.0

2021-02-16T06:53:36Z

PyGAD 2.10.2

2021-01-15T16:18:02Z

PyGAD 2.10.2

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

PyGAD 2.10.1 Documentation

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](https://github.com/tfarrag2000) for pointing about that at [GitHub](ahmedfgad/KerasGA#1).

2.10.0: Link to TorchGA project at GitHub

2021-01-04T02:09:26Z

Link to TorchGA project at GitHub

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

PyGAD 2.9.0

2020-12-05T23:29:23Z

PyGAD 2.9.0

Changes in PyGAD 2.9.0 (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`.

2.8.1: Bug fix in applying crossover

2020-10-03T03:53:38Z

Bug fix in applying crossover

Bug fix in applying the crossover operation when the `crossover_probability` parameter is used.
Thanks to Eng. Hamada Kassem, RA/TA, Construction Engineering and Management, Faculty of Engineering, Alexandria University, Egypt: https://www.linkedin.com/in/hamadakassem

2.8.0

2020-09-20T21:49:13Z

2.7.2

2020-09-14T16:49:47Z