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# DAWKINS' BLIND WATCHMAKER -- "METHINKS IT IS LIKE A WEASEL" EXAMPLE
#
# Start with a random phrase. Successively:
# Breed a next generation by copying with small mutations.
# Choose the member of the next generation that most closely resembles
# the target phrase to start breeding the next generation.
# How many generations will it take before the target is reached?
from whrandom import random
# Parameters determining the problem space
Target = 'methinks it is like a weasel'
Alphabet = ' abcdefghijklmnopqrstuvwxyz'
# Parameters to play with
GenerationSize = 100
MutationChance = 1.0/28.0
IndexSet = range(len(Target)) # Used to speed up loops
def choice(sequence):
n = len(sequence)
i = int(random() * float(n)) % n
return sequence[i]
def random_phrase():
phrase = ''
for i in IndexSet:
phrase = phrase + choice(Alphabet)
return phrase
def mutate_phrase(phrase):
mutant = phrase
for i in range(int(0.5 + MutationChance*float(len(phrase)))):
i = choice(IndexSet)
c = choice(Alphabet)
mutant = mutant[:i] + c + mutant[i+1:]
#print `mutant`
return mutant
def matching_factor(phrase):
factor = 0
for i in IndexSet:
if phrase[i] = Target[i]: factor = factor + 1
return factor
def breed_generation(phrase):
generation = [phrase]
while len(generation) < GenerationSize:
generation.append(mutate_phrase(phrase))
return generation
def select_best(generation):
factor, selected = -1, ''
for phrase in generation:
f = matching_factor(phrase)
if f > factor:
factor, selected = f, phrase
return selected
def main():
gen = 0
phrase = random_phrase()
print gen, `phrase`
while phrase <> Target:
next_generation = breed_generation(phrase)
#print next_generation
phrase = select_best(next_generation)
gen = gen+1
print gen, `phrase`
main()
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