PyGAD-2.16.1

1. Reuse the fitness of previously explored solutions rather than recalculating them. This feature only works if save_solutions=True.
2. The user can use the tqdm library to show a progress bar. #50

import pygad
import numpy
import tqdm

equation_inputs = [4,-2,3.5]
desired_output = 44

def fitness_func(solution, solution_idx):
    output = numpy.sum(solution * equation_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

num_generations = 10000
with tqdm.tqdm(total=num_generations) as pbar:
    ga_instance = pygad.GA(num_generations=num_generations,
                           sol_per_pop=5,
                           num_parents_mating=2,
                           num_genes=len(equation_inputs),
                           fitness_func=fitness_func,
                           on_generation=lambda _: pbar.update(1))
    
    ga_instance.run()

ga_instance.plot_result()

3. Solved the issue of unequal length between the solutions and solutions_fitness when the save_solutions parameter is set to True. Now, the fitness of the last population is appended to the solutions_fitness array. #64
4. There was an issue of getting the length of these 4 variables (solutions, solutions_fitness, best_solutions, and best_solutions_fitness) doubled after each call of the run() method. This is solved by resetting these variables at the beginning of the run() method. #62
5. Bug fixes when adaptive mutation is used (mutation_type="adaptive"). #65