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. https://github.com/ahmedfgad/GeneticAlgorithmPython/discussions/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()

1. 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. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/64
   
   
2. 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. https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/62
   
   
3. Bug fixes when adaptive mutation is used (mutation_type="adaptive"). https://github.com/ahmedfgad/GeneticAlgorithmPython/issues/65