Minimal implementation of Dawkins Weasel in Python. The aim is to demonstrate that Evolution (Mutation + Selection) is different from pure chance.

dawkins_weasel.py

# DawkinsWeasel.py

import random


chars = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ .,-'
target = 'The aim is to demonstrate that the process that drives evolutionary systems, random variation combined with non-random cumulative selection, is different from pure chance.'
print len(chars), len(target)
#b
def randchar():
	i = random.randint(0,len(chars)-1)
	return chars[i]


p_change = .01
n_generation = 100


def lev(a, b):
    if not a: return len(b)
    if not b: return len(a)
    return min(lev(a[1:], b[1:])+(a[0] != b[0]), lev(a[1:], b)+1, lev(a, b[1:])+1)

def distance(a,b):
	#return lev(a,b)

	assert(len(a)==len(b))
	dist = len(a)
	for x,y in zip(a,b):
		if x==y:
			dist-=1
	return dist

def givebirth(parent):
	child = ''
	for c in parent:
		if random.random() < p_change:
			child += randchar()
		else:
			child += c
	return child

def dawkinsWeasel():
	mystring = ''.join([randchar() for _ in range(len(target))])

	distance(mystring, target)

	print mystring, distance(mystring, target)
	ix = 0
	while distance(mystring, target) > 0:
		ix+=1
		newstrings = [givebirth(mystring) for _ in range(n_generation)]
		beststring = mystring
		bestdist = distance(mystring, target)
		bestidx = -1
		for e, ns in enumerate(newstrings):
			if distance(ns, target) < bestdist:
				bestdist = distance(ns, target)
				bestidx = e
		if bestidx != -1:
			mystring = newstrings[bestidx]

		print ix,mystring, distance(mystring, target)

#print lev('abc','def')
dawkinsWeasel()

'''
a='aim1'
b='aim2'
import time

for x in range(1,10):
	t0=time.time()
	print x, lev(x*a,b), time.time()-t0
'''