num_generations         = 100
num_parents_mating      = 2

sol_per_pop             = 5000
num_genes               = 10

init_range_low          = 0
init_range_high         = 1

parent_selection_type   = "tournament"
keep_elitism            = 50

crossover_type          = "single_point"

mutation_type           = "random"
mutation_percent_genes  = 20

continious_gene_space   = {'low' :  0, 'high':   1}
discrete_gene_space_1   = np.linspace(0.3, 2.4, 8)
discrete_gene_space_2   = np.linspace(3, 12, 4)


def check_for_termination(ga_instance):  
     if ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] >= np.float64(2.0):
        return "stop"
    

def genetic_optimizer(fun):

    ga_instance = pygad.GA(num_generations      = num_generations,
                       num_parents_mating       = num_parents_mating,
                       fitness_func             = fun,
                       sol_per_pop              = sol_per_pop,
                       num_genes                = num_genes,
                       init_range_low           = init_range_low,
                       init_range_high          = init_range_high,
                       parent_selection_type    = parent_selection_type,
                       keep_elitism             = keep_elitism,
                       crossover_type           = crossover_type,
                       mutation_type            = mutation_type,
                       mutation_percent_genes   = mutation_percent_genes,
                       gene_space               = [continious_gene_space, continious_gene_space, continious_gene_space, continious_gene_space, continious_gene_space, continious_gene_space, continious_gene_space, continious_gene_space, discrete_gene_space_1, discrete_gene_space_2],
                       on_generation            = check_for_termination,
                       mutation_by_replacement  = False,
                       random_mutation_min_val  = -1,
                       random_mutation_max_val  = 1,
                       save_best_solutions      = True)
    
    ga_instance.run()

    solution            = ga_instance.best_solutions[-1]
    solution_fitness    = ga_instance.best_solutions_fitness[-1]

    num_completed_generations = ga_instance.generations_completed

    print('Completed Generation: ' + str(num_completed_generations))

    return solution_fitness, solution