Tags: Madhusankha/GeneticAlgorithmPython
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Link to TorchGA project at GitHub Link to TorchGA project at GitHub: https://github.com/ahmedfgad/TorchGA
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`.
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
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