- initial import

git-svn-id: http://moon:8086/svn/projects/GeneticAlgorithm@237 fda53097-d464-4ada-af97-ba876c37ca34

GeneticAlgorithm.py

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+import numpy as np
+
+class FinessEvaluator(object):
+	def __init__(self):
+		self.x = np.linspace(-10,10,21)
+		pass
+
+	def process(self, c):
+		mse = 0
+		for x in self.x:
+			y = c.process(x)
+			mse += (y - self.fx(x))**2
+
+		mse *= 1/len(self.x)
+		fit = 1/(1 + mse)
+		return fit
+
+	def fx(self, x):
+		y = 5 + 3 * x + 7 * x**2
+		return y
+
+class Cromosome(object):
+	def __init__(self, id, param=None):
+		self.id = id
+		self.fit = 0
+		self.fitAccum = 0
+		self.param = param
+
+	def process(self, x):
+		result = self.param[0] + self.param[1]*x + self.param[2]*x**2
+		return result[0]
+
+	def cross(self, p2):
+		a = 0.5
+		param1 = (1-a)*self.param + a*p2.param
+		param2 = (1-a)*p2.param + a*self.param
+		ca = Cromosome(self.id, param1)
+		cb = Cromosome(p2.id, param2)
+		return ca, cb
+
+	def mutate(self):
+		gene = int(np.random.rand() * 3)
+		allele = int(5*(0.5 - np.random.rand()))
+		self.param[gene] += allele
+
+	def __str__(self):
+		return "Id: {}, f={:0.6f}, fitAccum={:0.6f}, param=\n{}".format(self.id, self.fitness, self.fitAccum, str(self.param))
+
+
+class Population(object):
+	def __init__(self, numCromosomes):
+		self.bestFit = 0
+		self.sumFit = 0
+		self.cromosomes = []
+		for i in range(0, numCromosomes):
+			self.cromosomes.append(Cromosome(str(i), np.random.rand(3,1)))
+
+	def print(self):
+		print ("Population fitness  = {:0.6f}".format(self.sumFit))
+		print ("Best indiv. fitness = {:0.6f}".format(self.bestFit))
+
+	def process(self):
+		for c in self.cromosomes:
+			y = c.process(1)
+		return
+
+	def fitness(self, fitnessEval):
+		self.sumFit = 0
+		self.bestFit = 0
+		for c in self.cromosomes:
+			f = fitnessEval.process(c)
+			c.fitness = f
+			self.sumFit += f
+			if f > self.bestFit:
+				self.bestFit = f
+
+		# 1.) Sort by fitness
+		sorted = self.sort(self.cromosomes)
+
+		# 2.) Assign accumulated fitness
+		sum = 0
+		for c in sorted:
+			sum += c.fitness
+			c.fitAccum = sum
+
+		self.cromosomes = sorted
+
+		return self.sumFit
+
+	def mate(self, chanceOfReproduction=0.3):
+		newPop = self.cromosomes
+		count = 0
+		for i in range(0, int(len(self.cromosomes)/2)):
+			p = np.random.rand()
+
+			if p < chanceOfReproduction:
+				# 3.) Select parents
+				p1 = self.select(self.cromosomes)
+
+				p2 = p1
+				while(p2 == p1):
+					p2 = self.select(self.cromosomes)
+
+				ca, cb = p1.cross(p2)
+				newPop[count+0] = ca
+				newPop[count+1] = cb
+				count += 2
+
+		self.cromosomes = newPop
+		return newPop
+
+	def select(self, sorted):
+		p = self.sumFit * np.random.rand()
+		for c in sorted:
+			if c.fitAccum > p:
+				break
+
+		return c
+
+	def mutate(self, chanceOfMutation=0.06):
+		for c in self.cromosomes:
+			p = np.random.rand()
+
+			if p < chanceOfMutation:
+				c.mutate()
+
+		pass
+
+	def best(self):
+		return self.cromosomes[len(self.cromosomes)-1]
+
+	def starve(self):
+		pass
+
+	def sort(self, array):
+		less = []
+		equal = []
+		greater = []
+
+		if len(array) > 1:
+			pivot = array[0].fitness
+			for x in array:
+				if x.fitness < pivot:
+					less.append(x)
+				elif x.fitness == pivot:
+					equal.append(x)
+				elif x.fitness > pivot:
+					greater.append(x)
+			# Don't forget to return something!
+			return self.sort(less)+equal+self.sort(greater)  # Just use the + operator to join lists
+		# Note that you want equal ^^^^^ not pivot
+		else:  # You need to hande the part at the end of the recursion - when you only have one element in your array, just return the array.
+			return array
+
+
+if __name__ == '__main__':
+	f = FinessEvaluator()
+	p = Population(50)
+
+	for i in range(0, 500):
+		p.mutate(0.1)
+		p.fitness(f)
+		p.mate()
+		p.print()
+
+	p.fitness(f)
+	p.print()
+	print(p.best())
+	print ("End of Program")