class GENEALG:
def __init__(self, play_back=False):
if play_back:
f = open('robot.p', 'rb')
self.parent = pickle.load(f)
f.close()
else:
self.parent = POPULATION(c.popSize)
self.parent.Initialize()
self.parent.Evaluate()
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)
def Show_Best(self):
bestIndividual = self.parent.Best_Individual()
self.parent.p[bestIndividual].Start_Evaluation(pb=False, pp=True)
self.parent.p[bestIndividual].Compute_Fitness()
print(self.parent.p[bestIndividual].fitness)
def Save(self):
f = open('robot.p', 'wb')
pickle.dump(self.parent, f)
f.close()
from population import POPULATION
import copy
parents = POPULATION(10)
parents.Initialize() # 09
parents.Evaluate(pp=False, pb=True)
parents.Print()
for g in range(1, 200):
children = copy.deepcopy(parents)
children.Mutate()
children.Evaluate(pp=False, pb=True)
parents.ReplaceWith(children)
#parents.Sort()
print g,
parents.Print()
parents.Evaluate(pp=True, pb=False)
# print generation and time
print("\n results of generation: ", g)
print("\n evaluation time:", time.time() - startTime)
print("\n remaining time:", c.runTime - (time.time() - startTime))
# print results for each parent population
print("\n fitness of children 1 in envs 1:"),
children_1.Print()
print("\n fitness of children 2 in envs 2:"),
children_2.Print()
print("\n fitness of children 3 in envs 3:"),
children_3.Print()
print("\n")
#---REPLACE---#
parents_1.ReplaceWith(children_1)
parents_2.ReplaceWith(children_2)
parents_3.ReplaceWith(children_3)
#---TABULATE FITNESS---#
# add fitness of best individual at current generation to
# tabulated fitness list for plotting
parents_1.Tabulate_Fitness()
parents_2.Tabulate_Fitness()
parents_3.Tabulate_Fitness()
#---INCREMENT GENS---#
g += 1
###############
# PICKLE SAVE #
import copy
import pickle
from individual import INDIVIDUAL
from population import POPULATION
# import matplotlib.pyplot as plt
parent = POPULATION(5)
parent.Evaluate()
for g in range(100):
print(g, end=' ')
parent.Print()
child = copy.deepcopy(parent)
child.Mutate()
child.Evaluate()
parent.ReplaceWith(child)
# parent = INDIVIDUAL()
# parent.Evaluate(False)
# for g in range(0, 100):
# child = copy.deepcopy(parent)
# child.Mutate()
# child.Evaluate(True)
# print('[g: ', g, ' ] ', '[pw: ', parent.genome, ' ] ',
# '[p: ', parent.fitness, ' ] ', '[c: ', child.fitness, ' ]')
# if child.fitness > parent.fitness:
# parent = child
# parent.fitness = child.fitness
# parent.Evaluate(False)
# f = open('robot.p', 'wb')
# pickle.dump(parent, f)
# f.close()
parents.Evaluate(pb, 'doNotSave') # evaluate N unrelated robots in turn
print('Fitness values of the 1st generation of parents:')
parents.Print()
print # end the current line of printing
print('Fitness values of consequent generations of parents:')
for g in range(0, G): # iterate over generations 0 to G
# create a population of children
children = copy.deepcopy(parents) # create children which are clones of the parents
children.Mutate() # add mutations to the children population
children.Evaluate(pb, 'doNotSave') # evaluate N unrelated robots in turn
parents.ReplaceWith(children) # replace less fit parents with their more fit children; leave more fit parents in the parent population
print(g),
parents.Print()
parents.Evaluate(False, 'save') # re-evaluate the last generation of parents and draw them to the screen
# f = open('lastGen.p', 'w') # open a file called robot.p
# pickle.dump(parents, f) # save the parents class instance into this file
# f.close() # close the file
#f = plt.figure() # create a figure
#panel = f.add_subplot(111) # add a drawing panel inside that figure
#plt.plot(sensorData) # plot the data in the vector to the panel
##panel.set_ylim(-1,2) # move the lower limit of the vertical limit to -1 and the upper limit to +2
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),
children2.Print()
if g == (c.numGens - 1):
