Simple ga

ga.py

#### Simple genetic algorithm
#### Joel Lehman

import random
errors=[]

def equation_error(x):
 left_side = 2.0*x-10.0
 right_side = 3.0
 return abs(left_side-right_side)

def score_dna(genes):
 return [equation_error(x) for x in genes]

def make_next_generation(genes,scores):
 together=zip(scores,genes)
 together.sort()
 next_gen=[]
 for x in range(5):
  for copies in range(2):
   next_gen.append(together[x][1]+random.uniform(-0.1,0.1))
 return next_gen

#make initial list of DNAs
DNAs=[]
#ten times add a random number between 0 and 20 to the list
for x in range(10):
 DNAs.append(random.uniform(0.0,20.0))

generation=1
error=100000.0
while(error>0.01):
 scores=score_dna(DNAs)
 error=min(scores)
 best=DNAs[scores.index(error)]
 print "Loop #",generation, " Lowest error: %0.2f" %error, " Best DNA: %0.2f" %best
 DNAs = make_next_generation(DNAs,scores)
 generation+=1
 errors.append(error)

#this part requires matplotlib be installed
#you can comment it out and still run the ga
from pylab import *
title("Error vs. Time")
xlabel("Times through loop")
ylabel("Lowest error")
plot(errors)
show()