def experiment1(self): l = GA(self.fitnessFunction, self.myNetwork.params[self.indices]) l.minimize = False l.verbose = True l.maxLearningSteps = 500 params, fitness = l.learn() self.myNetwork.params[self.indices] = params self.metaInfo["numsteps"] = l.maxLearningSteps self.metaInfo["fitness"] = fitness # self.myNetwork._setParameters(self.originalWeights) self.logNet()
popList.append(initPopulation) for x in range(0,numberOfAgents): weights = initPopulation[0] NNetListParams.append(weights) i = 0 while (i < 50): for x in range(0,len(NNetListParams)): for k in range(0,len(NNetListParams)): NNetList[k]._setParameters(NNetListParams[k]) workingNN = x ga = GA(evaluator,NNetListParams[x],maxEvaluations = 20,initRangeScaling = 2,elitism = False,populationSize = populationSize,initialPopulation = popList[x],mutationProb = 0.1) ga.minimize = True result = ga.learn() NNetListParams[x] = result[0] popList[x] = ga.currentpop i += 1 print i for x in range(0,len(NNetListParams)): for k in range(0,len(NNetListParams)): NNetList[k]._setParameters(NNetListParams[k])