def ConvergencePlot(waveform,
input_num,
mating_parent_number,
max_gen=20,
convergence_point=100000):
with open("GensPerPopSize.csv", mode="a") as GenCSV:
GenPPS = csv.writer(GenCSV, delimiter=',')
GensPerPopSize = []
PopSize = []
for population_size in range(5, 50):
generation = 1
# Defining the population size.
pop_size = (
population_size, input_num
) # The population will have sol_per_pop chromosome where each chromosome has num_weights genes.
#Creating the initial population.
new_population = ga.create_population(pop_size)
fitness = ga.cal_pop_fitness(waveform, new_population)
while (np.max(fitness) < convergence_point):
# Measuring the fitness of each chromosome in the population.
fitness = ga.cal_pop_fitness(waveform, new_population)
# Selecting the best parents in the population for mating.
parents = ga.select_mating_pool(new_population, fitness,
mating_parent_number)
# Generating next generation using crossover.
offspring_crossover = ga.crossover(
parents,
offspring_size=(pop_size[0] - parents.shape[0], input_num))
# Adding some variations to the offspring using mutation.
offspring_mutation = ga.mutation(offspring_crossover,
num_mutations=2)
# Creating the new population based on the parents and offspring.
new_population[0:parents.shape[0], :] = parents
new_population[parents.shape[0]:, :] = offspring_mutation
generation += 1
if (generation > max_gen):
print("{} Did Not Converge".format(population_size))
generation = max_gen
break
GensPerPopSize.append(generation)
PopSize.append(population_size)
GenPPS.writerow([population_size, generation])
print("{} Population Size Complete".format(population_size))
return GensPerPopSize, PopSize
def GA_filter(waveform, input_num, solutions_per_population,
mating_parent_number, num_generations):
# Defining the population size.
pop_size = (
solutions_per_population, input_num
) # The population will have sol_per_pop chromosome where each chromosome has num_weights genes.
#Creating the initial population.
new_population = ga.create_population(pop_size)
best_outputs = []
for generation in range(num_generations):
# Measuring the fitness of each chromosome in the population.
fitness = ga.cal_pop_fitness(waveform, new_population)
# The best result in the current iteration.
best_outputs.append(np.max(fitness))
# Selecting the best parents in the population for mating.
parents = ga.select_mating_pool(new_population, fitness,
mating_parent_number)
# Generating next generation using crossover.
offspring_crossover = ga.crossover(
parents,
offspring_size=(pop_size[0] - parents.shape[0], input_num))
# Adding some variations to the offspring using mutation.
offspring_mutation = ga.mutation(offspring_crossover, num_mutations=2)
# Creating the new population based on the parents and offspring.
new_population[0:parents.shape[0], :] = parents
new_population[parents.shape[0]:, :] = offspring_mutation
# if (generation < 20):
# print("{}\n {}\n\n".format(new_population, pop_fitness))
if (generation % 10 == 0 and generation != 0):
print("{} Generations Completed".format(generation))
# Getting the best solution after iterating finishing all generations.
#At first, the fitness is calculated for each solution in the final generation.
fitness = ga.cal_pop_fitness(waveform, new_population)
# Then return the index of that solution corresponding to the best fitness.
best_match_idx = np.where(fitness == np.max(fitness))[0]
return new_population[
best_match_idx, :], fitness[best_match_idx][0][0], best_outputs
