gistfile1.py

import random

class DNA:
  def __init__(self):
		self.genes = []
		self.fit = 0.0
		
		for i in range(0,len(target)):
			self.genes.append(chr(random.randrange(32,128)))
			
	def fitness(self):
		ltarget = list(target)
		score = 0
		for i in range(0,len(self.genes)):
			if(self.genes[i] == ltarget[i]):
				score = score + 1
		
		if(score > 0):
			self.fit = score / len(ltarget)
		
	def crossover(self, partner):
		child = DNA()
		midpoint = int(random.randrange(len(self.genes)))
		for i in range(0, len(self.genes)):
			if(i > midpoint):
				child.genes[i] = self.genes[i]
			else:
				child.genes[i] = partner.genes[i]
				
		return child
		
	def mutate(self):
		for i in range(0, len(self.genes)):
			original = self.mutatedTarget()
			if(random.uniform(0, 1) < mutationRate):
				self.genes[i] = chr(random.randrange(32,128))
		
		self.fitness()
				
	def mutatedTarget(self):
		return str.join("", self.genes)
	
mutationRate = 0.02
totalPopulation = 1000
target = "Adolf Hitler"
population = []	

def setup():
	for i in range(0,totalPopulation):
		population.append(DNA())
		
def draw():
	matingPool = []
	for pop in population:
		pop.fitness()
		n = int(pop.fit * 100)
		for j in range(0, n):
			matingPool.append(pop)
		
	for i in range(0, len(population)):
		a = random.randrange(len(matingPool))
		b = random.randrange(len(matingPool))
		while b == a:
			b = random.randrange(len(matingPool))

		parentA = matingPool[a]
		parentB = matingPool[b]
		child = parentA.crossover(parentB)
		child.mutate()
		
		population[i] = child


setup()
best = population[0]
i = 0
while(best.mutatedTarget() != target):
	draw()
	
	bestFit = 0.0
	for p in population:
		if p.fit > bestFit:
			bestFit = p.fit
			best = p
	print("Generation " + str(i + 1) + ":")
	print(best.mutatedTarget())
	i = i + 1