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delay_after_genparameter is removed from thepygad.GAclass constructor. As a result, it is no longer an attribute of thepygad.GAclass instances. To add a delay after each generation, apply it inside theon_generationcallback. delay_after_gen warning #283single_point_crossover()method of thepygad.utils.crossover.Crossoverclass, all the random crossover points are returned before theforloop. This is by calling thenumpy.random.randint()function only once before the loop to generate all the K points (where K is the offspring size). This is compared to calling thenumpy.random.randint()function inside theforloop K times, once for each individual offspring.examples/example_custom_operators.pyscript. Fix a typo in example_custom_operators #285pygad.torchga.predict()function, no gradients are calculated.gene_typeparameter of thepygad.helper.unique.Unique.unique_int_gene_from_range()method accepts the type of the current gene only instead of the full gene_type list.unique_float_gene_from_range()inside thepygad.helper.unique.Uniqueclass to find a unique floating-point number from a range.pygad.helper.unique.Unique.unique_gene_by_space()method to return the numeric value only instead of a NumPy array.pygad/helper/unique.pyscript to remove duplicate codes and reformatting the docstrings.initial_populationnot effectively used/retained for multiobjective problems? #279Matplotliblibrary is only imported when a method inside thepygad/visualize/plot.pyscript is used. This is more efficient than usingimport matplotlib.pyplotat the module level as this causes it to be imported whenpygadis imported even when it is not needed. Matplotlib imported by pyGAD #292stop_criteriaparameter (e.g.stop_criteria=["saturate_10", "reach_-0.5"]). "reach" stop criteria with negative valued fitness function #296self.best_solutionsis a list of lists inside thecal_pop_fitnessmethod. 'numpy.ndarray' object has no attribute 'index' #293cal_pop_fitness()method was using theprevious_generation_fitnessattribute to return the parents fitness. This instance attribute was not using the fitness of the latest population, instead the fitness of the population before the last one. The issue is solved by updating theprevious_generation_fitnessattribute to the latest population fitness before the GA completes. ga_instance.best_solution() only returning best parameters and fitness of previous Generation #291