import pygad
import numpy

function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
desired_output = 44 # Function output.

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

last_fitness = 0
def on_generation(ga_instance):
    global last_fitness
    print(f"Generation = {ga_instance.generations_completed}")
    print(f"Fitness    = {ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]}")
    print(f"Change     = {ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness}")
    last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]

if __name__ == '__main__':
    ga_instance = pygad.GA(num_generations=100,
                           num_parents_mating=10,
                           sol_per_pop=20,
                           num_genes=len(function_inputs),
                           fitness_func=fitness_func,
                           on_generation=on_generation,
                           # parallel_processing=['process', 2],
                           parallel_processing=['thread', 2]
                           )
    
    # Running the GA to optimize the parameters of the function.
    ga_instance.run()
    
    # Returning the details of the best solution.
    solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
    print(f"Parameters of the best solution : {solution}")
    print(f"Fitness value of the best solution = {solution_fitness}")
    print(f"Index of the best solution : {solution_idx}")