def get_fitness(n):
#print 'Getting fitness of ' + str(n)
return play(True, n)
def performance(n):
return play(False, n) # Returning the score of the current neural network
def get_new_pop(pop):
new_pop = []
pop = sorted(pop, key=get_fitness, reverse=True)
print str(pop[0])
new_pop.extend(pop[0:5])
while len(new_pop) < len(pop):
first = select(pop)
second = select(pop)
first, second = breed(first, second)
new_pop.extend([first, second])
return new_pop
## Code to play the game with previous weights assigned i.e; before the genetic algorithm is run
# test AI
#n = Net()
#good = [-.5524816518, -0.251785039, -1.2697808633, -0.72796955875826, -0.81684247849, 3.97877468847]
# good = [-1.0863879678143158, -0.38785036446364252, -2.4946001121260855, -1.5860913785563859, -1.7712810292659231, 8.1115617366593806]
#n.decode(good)
#play(False, n)
# start genetic algo
pop = init_pop()
for i in xrange(GENERATIONS):
print '==========================='
print 'GENERATION ' + str(i)
pop = get_new_pop(pop)
play(False, pop[0])
print str(pop[0])
def get_new_pop(pop):
new_pop = []
pop = sorted(pop, key=get_fitness,reverse=True)
print str(pop[0])
new_pop.extend(pop[0:5])
while len(new_pop) < len(pop):
first = select(pop)
second = select(pop)
first, second = breed(first, second)
new_pop.extend([first, second])
return new_pop
## Code to play the game with previous weights assigned i.e; before the genetic algorithm is run
# test AI
#n = Net()
#good = [-.5524816518, -0.251785039, -1.2697808633, -0.72796955875826, -0.81684247849, 3.97877468847]
# good = [-1.0863879678143158, -0.38785036446364252, -2.4946001121260855, -1.5860913785563859, -1.7712810292659231, 8.1115617366593806]
#n.decode(good)
#play(False, n)
# start genetic algo
pop = init_pop()
for i in xrange(GENERATIONS):
print '==========================='
print 'GENERATION ' + str(i)
pop = get_new_pop(pop)
play(False, pop[0])
print str(pop[0])
def get_fitness(n): #print 'Getting fitness of ' + str(n) return play(True, n)
def performance(n):
return play(False, n)
if performance(first) > performance(second):
return first
else:
return second
def newnetlist(genome):
newnetlist = []
genome = sorted(genome, key=performance, reverse=True)
newnetlist.extend(genome[0:5])
while len(newnetlist) < len(genome):
first = survivaloffittest(genome)
second = survivaloffittest(genome)
first, second = updateweights(first, second)
newnetlist.append(first)
newnetlist.append(second)
return newnetlist
genome = []
for i in range(0, netsize):
netelement = NN.NeuralNet()
genome.append(netelement)
for i in range(0, generations):
print 'GENERATION ' + str(i)
genome = newnetlist(genome)
#genome = sorted(genome, key=performance,reverse=True)
play(False, genome[0])
def performance(n):
#Parameter 'True' represents playing the game without displaying on screen
return play(True, n)
def performance(n):
#Parameter 'True' represents playing the game without displaying on screen
return play(True,n)
while len(newnetlist) < len(genome):
#Till the newgenome doesn't have 'netlist' number of NeuralNets
#Selecting 2 NeuralNets from the genome which have a good performance value by calling
#the survival of the fittest function defined above
first = survivaloffittest(genome)
second = survivaloffittest(genome)
#For these 2 NeuralNets, now updating their weights by calling the update weights function defined above
first, second = updateweights(first, second)
#Adding the updated weight NeuralNets to the newgenome
newnetlist.append(first)
newnetlist.append(second)
return newnetlist
n = NeuralNet()
good = [-.5492326596523, -0.2495625644, -1.272124584125, -0.7305451455, -0.809896551525, 3.9812152125252]
n.listtonet(good)
play(False, n)
# Running of the main genetic algorithm
#Initialising the genome by calling the oldnetlist function
genome = oldnetlist()
#Iterating over all the generations
for i in range(0,generations):
print 'GENERATION ' + str(i)
genome = newnetlist(genome)
#Playing the game after complete training in every generation
#Here the play function is called with parameter False which indicates showing it on the screen
play(False, genome[0])