def run1(self):
timeStep = 1
while timeStep <= self.timeSteps:
self.paramsGA1["simulator"].clear()
self.paramsGA1["timeSteps"] = timeStep
ga1 = GA1(self.paramsGA1)
bestIndividuals = ga1.run()
newPopulation = self.addTimeStep(bestIndividuals)
self.paramsGA1["populationGA1"] = newPopulation
if timeStep <= self.timeSteps//2:
timeStep *= 2
elif timeStep < self.timeSteps and timeStep > self.timeSteps//2:
timeStep = self.timeSteps
elif timeStep == self.timeSteps:
break
bestIndividual = newPopulation[0][0:self.params["crossroads"]]
self.params["simulator"].clear()
self.params["simulator"].setState(bestIndividual)
fitness = 0
for timeStep in range(self.params["timeSteps"]):
population = []
for individual in newPopulation:
population.append(
individual[timeStep*self.params["crossroads"]:(timeStep+1)*self.params["crossroads"]])
print("Timtestep: " + str(timeStep))
self.paramsGA2["densities"] = None
self.paramsGA2["population"] = population
ga2 = GA2(self.paramsGA2)
best, bestIndividual = ga2.run()
fitness += best
self.params["simulator"].setState(bestIndividual)
print(fitness)
self.params["simulator"].exit()
return fitness
def __init__(self, params):
self.params = params
self.timeSteps = params["timeSteps"]
self.paramsListGA1 = ["crossover", "mutate", "select", "populationGA1", "numGeneration1",
"crossroads", "timeSteps", "numIndividuals1", "fitnessGA1", "simulator", "minLim", "maxLim"]
self.paramsGA1 = dict((k, params[k])
for k in self.paramsListGA1 if k in params)
self.ga1 = GA1(self.paramsGA1)
def run(self, params):
timeSteps = params["timeSteps"]
paramsListGA1 = ["crossover", "mutate", "select", "numGeneration1", "crossroads", "timeSteps", "numIndividuals1", "fitnessGA1", "simulator", "minLim", "maxLim"]
paramsGA1 = dict((k, params[k]) for k in paramsListGA1 if k in params)
ga1 = GA1(paramsGA1)
bestIndividual, score, improvement = ga1.run()
params["simulator"].exit()
return improvement
def addTimeStep(self, bestIndividuals):
ga1 = GA1(self.paramsGA1)
prevTimeSteps = (len(bestIndividuals[0])//self.paramsGA1["crossroads"])
if (prevTimeSteps) <= self.timeSteps//2:
newTimeSteps = prevTimeSteps*2
self.paramsGA1["timeSteps"] = newTimeSteps
for individual in bestIndividuals:
individual += individual
else:
newTimeSteps = self.timeSteps
self.paramsGA1["timeSteps"] = newTimeSteps
for individual in bestIndividuals:
individual += individual[len(individual)-(
self.paramsGA1["crossroads"]*(newTimeSteps - prevTimeSteps)):len(individual)]
ga1 = GA1(self.paramsGA1)
newPopulation = ga1.makePopulation(bestIndividuals)
del ga1
return newPopulation
def run2(self):
self.paramsGA1["simulator"].clear()
ga1 = GA1(self.paramsGA1)
fitness, improvement, individual = ga1.run()
individual = individual[0]
positions = {}
for i in range(self.timeSteps):
positions[i] = self.paramsGA1["simulator"].getPositions(
individual[i * self.params["crossroads"]:(i + 1) *
self.params["crossroads"]])
self.params["simulator"].exit()
return positions, individual, improvement
def run1(self):
timeStep = 1
while timeStep <= self.timeSteps:
self.paramsGA1["simulator"].clear()
self.paramsGA1["timeSteps"] = timeStep
ga1 = GA1(self.paramsGA1)
bestIndividuals = ga1.run()
newPopulation = self.addTimeStep(bestIndividuals)
self.paramsGA1["populationGA1"] = newPopulation
if timeStep <= self.timeSteps // 2:
timeStep *= 2
elif timeStep < self.timeSteps and timeStep > self.timeSteps // 2:
timeStep = self.timeSteps
elif timeStep == self.timeSteps:
break
self.params["simulator"].exit()
def run1(self):
timeStep = 0
newPopulation = []
while timeStep < self.timeSteps:
self.paramsGA1["simulator"].clear()
self.paramsGA1["timeSteps"] = timeStep
ga1 = GA1(self.paramsGA1)
bestIndividuals = ga1.run()
newPopulation = self.addTimeStep(bestIndividuals)
self.paramsGA1["populationGA1"] = newPopulation
if timeStep <= (self.timeSteps - 1) // 2:
timeStep *= 2
elif timeStep < (self.timeSteps -
1) and timeStep > (self.timeSteps - 1) // 2:
timeStep = (self.timeSteps - 1)
elif timeStep == (self.timeSteps - 1):
break
bestIndividual = newPopulation[0]
for i in range(self.timeSteps):
self.positions[i] = self.paramsGA1["simulator"].getPositions(
bestIndividual[i * self.params["crossroads"]:(i + 1) *
self.params["crossroads"]])
self.params["simulator"].exit()
return newPopulation