def __init__(self,obj,robot=False):
'''
Constructor.
'''
Solution.__init__(self, len(obj))
#another list for objectives /
#next to the one provided by the Superclass Solution?
self.selected4 = obj
self.grid_sz = -1
self.grid = 0 #4 personal history
self.history = []
self.objs=[0.0]*len(obj_names) #here i save everything for analysis
self.solver = False
self.BEHAV_DIM = 0
self.behaviorSamples=np.zeros(1)
self.IRARflag = False #true if IRAR has been evaluated
self.parentRar = 0
self.newInArchive = True
self.childrenInQ = 0
self.id = self._ids.next()
self.parentIDs = []
if(not robot):
self.robot=mazepy.mazenav()
self.robot.init_rand()
self.robot.mutate()
else:
self.robot=robot
def __init__(self, obj, robot=False):
'''
Constructor.
'''
Solution.__init__(self, len(obj))
#another list for objectives /
#next to the one provided by the Superclass Solution?
self.selected4 = obj
self.grid_sz = -1
self.grid = 0 #4 personal history
self.history = []
self.objs = [0.0] * len(
obj_names) #here i save everything for analysis
self.solver = False
self.BEHAV_DIM = 0
self.behaviorSamples = np.zeros(1)
self.IRARflag = False #true if IRAR has been evaluated
self.parentRar = 0
self.newInArchive = True
self.childrenInQ = 0
self.id = self._ids.next()
self.parentIDs = []
if (not robot):
self.robot = mazepy.mazenav()
self.robot.init_rand()
self.robot.mutate()
else:
self.robot = robot
def __init__(self, robot=False):
'''
Constructor.
'''
Solution.__init__(self, len(obj))
self.objs = [0.0] * 3
if (not robot):
self.robot = mazepy.mazenav()
self.robot.init_rand()
self.robot.mutate()
else:
self.robot = robot
def __init__(self,robot=False):
'''
Constructor.
'''
Solution.__init__(self, len(obj))
self.objs=[0.0]*3
if(not robot):
self.robot=mazepy.mazenav()
self.robot.init_rand()
self.robot.mutate()
else:
self.robot=robot
#function to calculate evolvability via PLoS paper metric
#do many one-step mutants from initial individual, see how many
#'unique' behaviors we get
def calc_evolvability(robot, grid_sz, mutations):
grids = set()
for x in range(mutations):
mutant = robot.copy()
mutant.mutate()
mutant.map()
grids.add(map_into_grid(mutant, grid_sz))
return len(grids)
if (__name__ == '__main__'):
#initialize maze stuff with "medium maze"
mazepy.mazenav.initmaze("hard_maze_list.txt")
mazepy.mazenav.random_seed()
#create initial genome
robot = mazepy.mazenav()
#initalize it randomly and mutate it once for good measure
robot.init_rand()
robot.mutate()
#run genome in the maze simulator
robot.map()
#calculate evolvability
print "evolvability:", calc_evolvability(robot, 30, 200)
import mazepy
from entropy import *
#simple fitness function -- how close do we get to the goal at the end of
#a simulation?
def fitness(robot):
return mazepy.feature_detector.state_entropy(robot)
#return -mazepy.feature_detector.end_goal(robot)
#initialize maze stuff with "medium maze"
mazepy.mazenav.initmaze("hard_maze_list.txt")
mazepy.mazenav.random_seed()
#create initial genome
robot=mazepy.mazenav()
#initalize it randomly and mutate it once for good measure
robot.init_rand()
robot.mutate()
#run genome in the maze simulator
robot.map()
#current fitness set to how fit our first dude is
cur_fitness=fitness(robot)
iterations=0
#loop until we discover a solution to the maze
#this is using simple hillclimbing algorithm (not neat)
newrobot=robot
while not newrobot.solution():
def optimize_evolvability(robot=None):
evo_fnc = calc_evolvability_entropy
#initialize maze stuff with "medium maze"
population = defaultdict(list)
whole_population = []
psize = 10
e_samp = 400
if (robot == None):
for k in range(psize):
robot = mazepy.mazenav()
robot.init_rand()
robot.mutate()
robot.map()
robot.fitness = evo_fnc(robot, e_samp)
whole_population.append(robot)
else:
for k in range(psize):
robot2 = robot.copy()
robot2.map()
robot2.fitness = evo_fnc(robot, e_samp)
whole_population.append(robot2)
solved = False
evals = psize
child = None
while not solved:
evals += 1
parent = random.choice(whole_population)
parent.fitness = evo_fnc(parent, e_samp)
child = parent.copy()
child.mutate()
child.map()
child.fitness = evo_fnc(child, e_samp)
if (child.solution()):
#solved=True
pass
whole_population.append(child)
whole_population.sort(key=lambda k: k.fitness, reverse=True)
to_kill = whole_population[-1]
#print to_kill.fitness
whole_population.remove(to_kill)
del to_kill
if (evals % 1 == 0):
print evals, calc_population_entropy(
whole_population
), whole_population[0].fitness, whole_population[-1].fitness
#run genome in the maze simulator
#calculate evolvability
print child.solution()
print "evolvability:", evo_fnc(child, 1000)
robot = mazepy.mazenav()
robot.init_rand()
robot.mutate()
robot.map()
print "evolvability:", evo_fnc(robot, 1000)
def optimize_evolvability(robot=None):
evo_fnc = calc_evolvability_entropy
#initialize maze stuff with "medium maze"
population=defaultdict(list)
whole_population=[]
psize=10
e_samp=400
if(robot==None):
for k in range(psize):
robot=mazepy.mazenav()
robot.init_rand()
robot.mutate()
robot.map()
robot.fitness=evo_fnc(robot,e_samp)
whole_population.append(robot)
else:
for k in range(psize):
robot2=robot.copy()
robot2.map()
robot2.fitness=evo_fnc(robot,e_samp)
whole_population.append(robot2)
solved=False
evals=psize
child=None
while not solved:
evals+=1
parent=random.choice(whole_population)
parent.fitness=evo_fnc(parent,e_samp)
child=parent.copy()
child.mutate()
child.map()
child.fitness=evo_fnc(child,e_samp)
if(child.solution()):
#solved=True
pass
whole_population.append(child)
whole_population.sort(key=lambda k:k.fitness,reverse=True)
to_kill=whole_population[-1]
#print to_kill.fitness
whole_population.remove(to_kill)
del to_kill
if(evals%1==0):
print evals,calc_population_entropy(whole_population),whole_population[0].fitness,whole_population[-1].fitness
#run genome in the maze simulator
#calculate evolvability
print child.solution()
print "evolvability:", evo_fnc(child,1000)
robot=mazepy.mazenav()
robot.init_rand()
robot.mutate()
robot.map()
print "evolvability:", evo_fnc(robot,1000)
