PyGAD-2.19.1

1. A new summary() method is supported to return a Keras-like summary of the PyGAD lifecycle.
2. A new optional parameter called fitness_batch_size is supported to calculate the fitness function in batches. If it is assigned the value 1 or None (default), then the normal flow is used where the fitness function is called for each individual solution. If the fitness_batch_size parameter is assigned a value satisfying this condition 1 < fitness_batch_size <= sol_per_pop, then the solutions are grouped into batches of size fitness_batch_size and the fitness function is called once for each batch. In this case, the fitness function must return a list/tuple/numpy.ndarray with a length equal to the number of solutions passed. #136.
3. The cloudpickle library (https://github.com/cloudpipe/cloudpickle) is used instead of the pickle library to pickle the pygad.GA objects. This solves the issue of having to redefine the functions (e.g. fitness function). The cloudpickle library is added as a dependancy in the requirements.txt file. #159
4. Support of assigning methods to these parameters: fitness_func, crossover_type, mutation_type, parent_selection_type, on_start, on_fitness, on_parents, on_crossover, on_mutation, on_generation, and on_stop. #92 #138
5. Validating the output of the parent selection, crossover, and mutation functions.
6. The built-in parent selection operators return the parent's indices as a NumPy array.
7. The outputs of the parent selection, crossover, and mutation operators must be NumPy arrays.
8. Fix an issue when allow_duplicate_genes=True. #39
9. Fix an issue creating scatter plots of the solutions' fitness.
10. Sampling from a set() is no longer supported in Python 3.11. Instead, sampling happens from a list(). Thanks Marco Brenna for pointing to this issue.
11. The lifecycle is updated to reflect that the new population's fitness is calculated at the end of the lifecycle not at the beginning. #154 (comment)
12. There was an issue when save_solutions=True that causes the fitness function to be called for solutions already explored and have their fitness pre-calculated. #160
13. A new instance attribute named last_generation_elitism_indices added to hold the indices of the selected elitism. This attribute helps to re-use the fitness of the elitism instead of calling the fitness function.
14. Fewer calls to the best_solution() method which in turns saves some calls to the fitness function.
15. Some updates in the documentation to give more details about the cal_pop_fitness() method. #79 (comment)