Created
December 17, 2017 07:27
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import random | |
import copy | |
import matplotlib.pyplot as plt | |
class GeneticAlgorithm: | |
def __init__(self): | |
self.chromosome_length = 50 | |
self.population = [] | |
self.n = 20 | |
self.crossovers = 5 | |
self.mutations = 5 | |
self.iterator = 0 | |
self.max_iterator = 500 | |
self.selection_cmp = None | |
def initial(self): | |
for i in range(self.n): | |
self.population.append([random.randrange(-5, 5) for _ in range(self.chromosome_length)]) | |
def stop_condition(self): | |
return self.iterator > self.max_iterator | |
def do_crossover(self): | |
chromo_a = self.population[random.randrange(0, self.n / 2)] | |
chromo_b = self.population[random.randrange(self.n / 2, self.n)] | |
random_index = random.randrange(self.chromosome_length) | |
new_chromo_a = chromo_a[:random_index] + chromo_b[random_index:] | |
new_chromo_b = chromo_b[:random_index] + chromo_a[random_index:] | |
self.population.append(new_chromo_a) | |
self.population.append(new_chromo_b) | |
def do_mutation(self): | |
for _ in range(self.mutations): | |
chromo = copy.copy(self.population[random.randrange(len(self.population))]) | |
index = random.randrange(len(chromo) - 1) | |
chromo[index] += random.randrange(-5, 5) | |
self.population.append(chromo) | |
def do_selection(self): | |
self.population.sort(cmp=self.selection_cmp) | |
self.population = self.population[:self.n] | |
@staticmethod | |
def fitness(chromo): | |
result = 0 | |
for chromosome in chromo: | |
result += 3 * (chromosome ** 4) - 4 * (chromosome ** 2) + 3 * chromosome - 7 | |
return result | |
def __iter__(self): | |
return self | |
def next(self): | |
if self.iterator < self.max_iterator: | |
i = self.iterator | |
self.iterator += 1 | |
return i | |
else: | |
raise StopIteration() | |
if __name__ == "__main__": | |
perfect_fitness_list = [] | |
ga = GeneticAlgorithm() | |
ga.initial() | |
ga.selection_cmp = lambda x, y: ga.fitness(x) - ga.fitness(y) | |
for iterator in ga: | |
ga.do_crossover() | |
ga.do_mutation() | |
ga.do_selection() | |
fitness_list = [ga.fitness(a) for a in ga.population] | |
fitness = max(fitness_list) | |
perfect_fitness_list.append([ga.iterator, fitness]) | |
fig, ax = plt.subplots() | |
ax.plot([a[0] for a in perfect_fitness_list], [a[1] for a in perfect_fitness_list]) | |
ax.set(xlabel='iterates', ylabel='perfect fitness', title='Genetic algorithm') | |
fig.savefig("plot.png") | |
plt.show() |
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