def Evolve(self):
for g in range(c.numGens):
print(g, end=' ')
self.parent.Print()
child = POPULATION(c.popSize)
child.Fill_From(self.parent)
child.Evaluate()
self.parent.ReplaceWith(child)
import pickle
from population import POPULATION
from individual import INDIVIDUAL
import constants as c
from environments import ENVIRONMENTS
from environment import ENVIRONMENT
envs = ENVIRONMENTS()
parents = POPULATION(c.popSize)
parents.Initialize()
parents.Evaluate(envs, pp=False, pb=True)
#
parents.Print()
for g in range(1, c.numGens):
children = POPULATION(c.popSize)
children.Fill_From(parents)
children.Evaluate(envs, pp=False, pb=True)
print(g),
children.Print(),
print(),
parents.ReplaceWith(children)
if (g == c.numGens - 1):
print(parents.p[0].fitness)
for e in range(0, c.numEnvs):
parents.p[0].Start_Evaluation(envs.envs[e], pp=False, pb=False)
parents.p[0].Compute_Fitness()
print("\nfitness of parents 2 in envs 2:"),
parents_2.Print()
print("\nfitness of parents 3 in envs 3:"),
parents_3.Print()
print("\n")
###################
# EVOLVE CHILDREN #
###################
startTime = time.time()
g = 0
while (time.time() - startTime < c.runTime):
# initialize children
children_1 = POPULATION(c.popSize)
children_1.Fill_From(parents_1)
#--------------------#
children_2 = POPULATION(c.popSize)
children_2.Fill_From(parents_2)
#--------------------#
children_3 = POPULATION(c.popSize)
children_3.Fill_From(parents_3)
# evaluate children in respective environment types
children_1.Evaluate(envs_1, True)
children_2.Evaluate(envs_2, True)
children_3.Evaluate(envs_3, True)
# print generation and time
print("\n results of generation: ", g)
print("\n evaluation time:", time.time() - startTime)
evalTimes = []
generations = []
fillTimes = []
entire_fill = []
recorder = Recorder()
children = None
for g in range(1, c.numGens):
start = time.time()
# generations.append(g)
starttime = time.time() - start
if g > 0:
del children
children = POPULATION(c.popSize)
entire_fill_temp = children.Fill_From(parents)
fillTime = time.time() - starttime
# pp.pformat(children.pop[0])
children.Evaluate(envs, pb=True, pp=False)
evaluateTime = time.time() - fillTime
parents = copy.deepcopy(children)
copyTime = time.time() - evaluateTime
if g % 2 == 0:
print(g, end=" ")
children.Print()
else:
print(g)
recorder.record_times(g, evaluateTime, copyTime, fillTime, starttime,
entire_fill_temp)
recorder.add_metrics(parents.pop[0])
import pickle
import constants as c
from population import POPULATION
# import matplotlib.pyplot as plt
parent = POPULATION(c.popSize)
parent.Initialize()
parent.Evaluate(True)
for g in range(c.numGens):
print(g, end=' ')
parent.Print()
child = POPULATION(c.popSize)
child.Fill_From(parent)
child.Evaluate()
parent.ReplaceWith(child)
bestIndividual = parent.Best_Individual()
parent.p[bestIndividual].Start_Evaluation(pb=False, pp=True)
parent.p[bestIndividual].Compute_Fitness()
f = open('robot.p', 'wb')
pickle.dump(parent, f)
f.close()
envs = ENVIRONMENTS()
parents = POPULATION(c.popSize)
parents.Initialize()
parents.Evaluate(envs, True, True, c.evalTime)
parents.Print()
parents2 = POPULATION(c.popSize)
parents2.Initialize2()
parents2.Evaluate(envs, True, True, c.evalTime)
parents2.Print()
for g in range(1, c.numGens):
children = POPULATION(c.popSize)
children.Fill_From(parents)
# children.Fill_From2(parents)
children.Evaluate(envs, False, True, c.evalTime)
parents.ReplaceWith(children)
# parents.ReplaceWith2(children)
print('Whegged: '),
print(g),
children.Print()
children2 = POPULATION(c.popSize)
children2.Fill_From(parents2)
children2.Evaluate(envs, False, True, c.evalTime)
parents2.ReplaceWith(children2)
print('Wheeled: '),
print(g),
