A python library for genetic algorithms
import string
import random
import genetics
letters = string.ascii_uppercase + string.ascii_lowercase + string.punctuation + ' '
solution = 'Hello World!'
class LetterComponent(genetics.DNAComponent):
def mutate_value(self):
return random.choice(letters)
class WordDNA(genetics.arrayed_segment(len(solution), LetterComponent)):
@property
def score(self):
return sum(1 if comp.value == letter else 0 for comp, letter in
zip(self, solution))
def __str__(self):
return ''.join(comp.value for comp in self)
sim = genetics.DiscreteSimulation(
population_size=100,
mutation_mask=genetics.mutation_rate(0.01),
crossover_mask=genetics.one_point_crossover,
selection_function=genetics.tournament(2),
elite_size=2,
initial_generator=WordDNA)
def dna_stats(population):
'''Best DNA, best score, average score'''
best_dna = max(population)
best_score = best_dna.score
average_score = sum(member.score for member in population) / len(population)
return best_dna, best_score, average_score
population = sim.initial_population()
while True:
best, best_score, average_score = dna_stats(population)
print('{} | Average score: {}'.format(str(best), average_score))
if str(best) == solution:
break
population = sim.step(population)Sample Output:
&~$lo,{j'"wi | Average score: 0.148
&~$lo,{j'"wi | Average score: 0.292
H)Xl] ?lDf{@ | Average score: 0.506
H)Xlo {lZ!&@ | Average score: 0.816
H)Xlo {lZ!&@ | Average score: 1.154
HSlldpWjr`z> | Average score: 1.574
HeXyoKWqrl&K | Average score: 2.136
uello*c,rl"! | Average score: 2.722
uello*c,rl"! | Average score: 3.338
Heslo LKrlk! | Average score: 3.814
Heslo LKrlk! | Average score: 4.492
H(llooWIrld! | Average score: 5.258
Hello W,rl]! | Average score: 5.934
HeDlo World! | Average score: 6.628
HeDlo World! | Average score: 7.362
Hello World! | Average score: 8.004