Data commit

Task/Evolutionary-algorithm/Python/evolutionary-algorithm-1.py

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+from string import letters
+from random import choice, random
+
+target  = list("METHINKS IT IS LIKE A WEASEL")
+charset = letters + ' '
+parent  = [choice(charset) for _ in range(len(target))]
+minmutaterate  = .09
+C = range(100)
+
+perfectfitness = float(len(target))
+
+def fitness(trial):
+    'Sum of matching chars by position'
+    return sum(t==h for t,h in zip(trial, target))
+
+def mutaterate():
+    'Less mutation the closer the fit of the parent'
+    return 1-((perfectfitness - fitness(parent)) / perfectfitness * (1 - minmutaterate))
+
+def mutate(parent, rate):
+    return [(ch if random() <= rate else choice(charset)) for ch in parent]
+
+def que():
+    '(from the favourite saying of Manuel in Fawlty Towers)'
+    print ("#%-4i, fitness: %4.1f%%, '%s'" %
+           (iterations, fitness(parent)*100./perfectfitness, ''.join(parent)))
+
+def mate(a, b):
+    place = 0
+    if choice(xrange(10)) < 7:
+        place = choice(xrange(len(target)))
+    else:
+        return a, b
+
+    return a, b, a[:place] + b[place:], b[:place] + a[place:]
+
+iterations = 0
+center = len(C)/2
+while parent != target:
+    rate = mutaterate()
+    iterations += 1
+    if iterations % 100 == 0: que()
+    copies = [ mutate(parent, rate) for _ in C ]  + [parent]
+    parent1 = max(copies[:center], key=fitness)
+    parent2 = max(copies[center:], key=fitness)
+    parent = max(mate(parent1, parent2), key=fitness)
+que()