A basic paradigm for genetic algorithms.

GeneticAlgDemo.py

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from pylab import *
def QuickerPlot(x):
	close()
	y=[]
	for k in range(1,len(x)+1):
		y.append(k)
	plot(y,x)
	xlabel('x')
	ylabel('y')
	grid(True)
	formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
	ax = subplot(111)
	ax.yaxis.set_major_formatter(formatter)
	ax.xaxis.set_major_formatter(formatter)
	show()

def ran():
	import random
	r = float(random.random())
	r = (r-0.5)*2.0
	return(r)

ans=[]

a = 0
for k in range(0,10,1):
	a += ran()

print 'Pure random 10 itterations: ' + str(a)
ans.append(a)

a = 0
for k in range(0,10,1):
	t1 = ran()
	t2 = ran()
	a += t1 if t1>=t2 else t2

print 'genetic algorithm degree 2, 10 itterations: ' + str(a)
ans.append(a)

for k in range(3,10):
	a=0
	for k2 in range(0,10):
		t=[]
		for k3 in range(0,k):
			t.append(ran())
		a+=max(t)
	print 'genetic algorithm degree '+str(k)+', 10 itterations: ' + str(a)
	ans.append(a)

QuickerPlot(ans)