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](https://github.com/ahmedfgad/KerasGA/issues/1).