for i in range(len(x.genes)):
weight += x.genes[i][0]
value += x.genes[i][1]
if weight <= 15:
return value
return 0
def print_func(x):
result = ""
count = collections.Counter(x)
for repetitions in count.keys():
if count.get(repetitions) != 0 and repetitions[0] != 0:
result += str(count.get(repetitions)) + \
"x" + str(repetitions) + ", "
return result
if __name__ == "__main__":
pop = Population(1000, 0.1, fitness_f, print_func)
pop.generate_individuals(gene_f, 15)
y1, y2, y3 = pop.evolve(iterations=ITERATIONS)
plt.plot(range(pop.generation - 1), y1)
plt.plot(range(pop.generation - 1), y2)
plt.plot(range(pop.generation - 1), y3)
plt.legend(["minimum fitness", "average fitness", "maximum fitness"],
loc=1)
plt.ylabel("fitness")
plt.xlabel("generation")
plt.show()
ITERATIONS = 50
"""search for the binary string specified here"""
word = list('0010101011010001001011')
def gene_f():
"""a gene is represented with a 1 or 0"""
return random.choice(['0', '1'])
def fitness_f(x):
"""the fitness function compares the position of each gene
with the optimal"""
n = 0
for i in range(len(x.genes)):
if word[i] == x.genes[i]:
n += 1
return n
def print_func(x):
result = ""
for gene in x:
result += gene
return result + ", "
if __name__ == "__main__":
pop = Population(100, 0.01, fitness_f, print_func)
pop.generate_individuals(gene_f, len(word))
pop.evolve(iterations=ITERATIONS, fitness_limit=len(word))