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diff --git a/lib/selection.py b/lib/selection.py
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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()