def test_new_population(self): ''' Make sure population can clone a new population. ''' population = Population(indv_template=self.indv_template, size=10) population.init() new_population = population.new() self.assertEqual(new_population.size, 10) self.assertListEqual(new_population.individuals, [])
def tain_svm(): indv_template = BinaryIndividual(ranges=[(-8, 8), (-8, 8), (-8, 8)], eps=[0.001, 0.001, 0.001]) population = Population(indv_template=indv_template, size=1000) population.init() # Initialize population with individuals. # In[ ]: selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) # mutation = FlipBitMutation(pm=0.1) mutation = FlipBitBigMutation(pm=0.1, pbm=0.55, alpha=0.6) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput, FitnessStore]) ############################################################# indv = engine.population.best_indv(engine.fitness).variants c, e, g = indv.variants[1], indv.variants[2], indv.variants[-1] clf = svm.svR(C=c, epsilon=e, gamma=g, kernel='rbf') data_x, data_y = preprocess_pca() clf.fit(data_x, data_y) predictval = clf.predict(data_x) reaval = data_y print(predictval) # In[ ]: engine.run(ng=100)
def ga(df, start, end, _positionList, ranges=[(20,100),(0.01, 1),(0.01, 1),(0.01, 1),(1, 5)], eps=0.01): indv_template = BinaryIndividual(ranges=ranges, eps=eps) population = Population(indv_template=indv_template, size=100) population.init() # Initialize population with individuals. # Use built-in operators here. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.3) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) @engine.fitness_register def fitness(indv): n, upper, lower, adds, cutoff = indv.solution df['KAMA'] = talib.KAMA(df.close, int(n)) df['VAR'] = talib.VAR(df.close-df.KAMA.shift(1) - df.close.shift(1)+df.KAMA.shift(2),10) profitsList, buypriceList, sellpriceList, fits,positionList = profitsCal(df, start, end, _positionList, upper=upper, lower=lower, adds = adds, cutoff=cutoff) return float(fits) @engine.analysis_register class ConsoleOutput(OnTheFlyAnalysis): master_only = True interval = 1 def register_step(self, g, population, engine): best_indv = population.best_indv(engine.fitness) msg = 'Generation: {}, best fitness: {:.3f}'.format(g, engine.fmax) print(best_indv.solution) engine.logger.info(msg) engine.run(ng=30) return population.best_indv(engine.fitness).solution, _positionList
def test_all_fits(self): population = Population(indv_template=self.indv_template, size=10) population.init() all_fits = population.all_fits(fitness=self.fitness) self.assertEqual(len(all_fits), 10) for fit in all_fits: self.assertTrue(type(fit) is float)
def generate(self): best_policy = None best_reward = -float('Inf') candidates = [] eps = 1 # equal to actions space resolution, eps is step size pop_size = 4 cross_prob = 1 exchange_prob = 1 mutation_pob = 1 generation = 4 tmp_reward = [] tmp_policy = [] random.seed(54) turb = 5 try: # Agents should make use of 20 episodes in each training run, if making sequential decisions # Define population indv_template = DecimalIndividual(ranges=[(0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1),(0, 1), (0, 1)], eps=eps) population = Population(indv_template=indv_template, size = pop_size) population.init() # Initialize population with individuals. # Create genetic operators # Use built-in operators here. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=cross_prob, pe=exchange_prob) # PE = Gene exchange probability mutation = FlipBitMutation(pm=mutation_pob) # 0.1 todo The probability of mutation # Create genetic algorithm engine to run optimization engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation,) # Define and register fitness function @engine.fitness_register def fitness(indv): p = [0 for _ in range(10)] p = indv.solution policy = {'1': [p[0], p[1]], '2': [p[2], p[3]], '3': [p[4], p[5]], '4': [p[6], p[7]], '5': [p[8], p[9]]}xw reward = self.environment.evaluatePolicy(policy) # Action in Year 1 only print('Sequential Result : ', reward) tmp_reward.append(reward) tmp_policy.append(policy) tmp_single = [] return reward + uniform(-turb, turb) # run engine.run(ng = generation) best_reward = max(tmp_reward) best_policy = tmp_policy[-pop_size] except (KeyboardInterrupt, SystemExit): print(exc_info()) return best_policy, best_reward
def test_selection(self): indv = BinaryIndividual(ranges=[(0, 30)]) p = Population(indv) p.init() selection = TournamentSelection() father, mother = selection.select(p, fitness=self.fitness) self.assertTrue(isinstance(father, BinaryIndividual)) self.assertTrue(isinstance(mother, BinaryIndividual))
def test_initialization(self): ''' Make sure a population can be initialized correctly. ''' population = Population(indv_template=self.indv_template, size=10) self.assertListEqual(population.individuals, []) population.init() self.assertEqual(len(population.individuals), 10) # Check individual. self.assertTrue(isinstance(population[0], BinaryIndividual))
def doga(units, ploads, hload, size=50, ng=5, pc=0.8, pe=0.5, pm=0.1): # 计算不同典型日下,最小运行成本均值 def calcost(indv): # 输入改造方案 for chpunit, rtype in zip(chpunits, indv): chpunit.rtype = rtype meancost = 0 # ploads为字典,key:典型日出现的概率,value:[典型日负荷曲线,风电出力极限曲线1,...,风电出力极限曲线n] for p in ploads.keys(): x = ProSimu() x.pload = ploads[p][0] x.hload = hload wn = 0 for wpunit in wpunits: wn += 1 wpunit.maxwp = ploads[p][wn] x.units = units meancost += x.getoptvalue()*p return meancost ranges = list() wpunits = list() chpunits = list() for unit in units: if unit.ptype == 0: # Wind Power Unit wpunits += [unit] elif unit.ptype == 2: # CHP Unit-1 ranges += [(0, 4)] chpunits += [unit] elif unit.ptype == 3: # CHP Unit-2 ranges += [(0, 3)] chpunits += [unit] template = BinaryIndividual(ranges, eps=1) population = Population(indv_template=template, size=size).init() selection = TournamentSelection() crossover = UniformCrossover(pc=pc, pe=pe) mutation = FlipBitMutation(pm=pm) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput, FitnessStore]) @engine.fitness_register @engine.minimize def fitness(indv): # print(type(float(calcost(indv.solution)))) return float(calcost(indv.solution)) engine.run(ng=ng) bestindv = population.best_indv(engine.fitness).solution for unitt, rtype in zip(chpunits, bestindv): unitt.rtype = rtype result = calcost(bestindv) print('Best individual:', bestindv) print('Optimal result:', result)
def test_selection(self): indv = BinaryIndividual(ranges=[(0, 30)]) p = Population(indv) p.init() selection = RouletteWheelSelection() father, mother = selection.select(p, fitness=self.fitness) self.assertTrue(isinstance(father, BinaryIndividual)) self.assertTrue(isinstance(mother, BinaryIndividual)) self.assertNotEqual(father.chromsome, mother.chromsome)
def tune_weights(self): old_fitness = self.individual.fitness weights_scaling = self.individual.get_subtree_scaling() weights_translation = self.individual.get_subtree_translation() # Create array with range for each scaling and translation parameter range = [ self.scale_range, ] * len(weights_scaling) + [ self.translation_range, ] * len(weights_translation) indv_template = DecimalIndividual(ranges=range, eps=0.1) population = Population(indv_template=indv_template, size=self.pop_size) population.init() engine = GAEngine( population=population, selection=TournamentSelection(), crossover=GaussianCrossover(pc=1.0), mutation=NoMutation(), fitness=self.fitness_function_GAFT, analysis=[ new_early_stopping_analysis(scale_range=self.scale_range) ]) engine.logger = NoLoggingLogger() # Run the GA with the specified number of iterations try: engine.run(ng=self.max_iterations) except ValueError: pass # Get the best individual. best_indv = engine.population.best_indv(engine.fitness) # Log the tuning process print( f"Tuner {np.round(old_fitness, 3)} {np.round(-engine.ori_fmax, 3)}" ) # Only use the new individual if it was really improved if old_fitness > -engine.ori_fmax: weights_scaling, weights_translation = self.split_list( best_indv.solution) self.individual.set_subtree_scaling(weights_scaling) self.individual.set_subtree_translation(weights_translation) self.individual.fitness = -engine.ori_fmax return deepcopy(self.individual)
def test_run(self): ''' Make sure GA engine can run correctly. ''' indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001) population = Population(indv_template=indv_template, size=50).init() # Create genetic operators. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) # Create genetic algorithm engine. engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation) @engine.fitness_register @engine.dynamic_linear_scaling() def fitness(indv): x, = indv.solution return x + 10 * sin(5 * x) + 7 * cos(4 * x) engine.run(50)
def __init__(self ,k ,total_implied_variance ,slice_before ,slice_after ,tau): self.k =k self.total_implied_variance =total_implied_variance self.slice_before =slice_before self.slice_after = slice_after self.tau = tau # Define population. indv_template = BinaryIndividual(ranges=[(1e-5, 20),(1e-5, 20),(1e-5, 20)], eps=0.001) self.population = Population(indv_template=indv_template, size=30).init() # Create genetic operators. selection = TournamentSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) # Create genetic algorithm engine. self.engine = GAEngine(population=self.population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) # Define fitness function. @self.engine.fitness_register @self.engine.minimize def fitness(indv): a, b, m, rho, sigma = indv.solution model_total_implied_variance=svi_raw(self.k,np.array([a, b, m, rho, sigma]),self.tau) value = norm(self.total_implied_variance - model_total_implied_variance,ord=2) # if bool(len(self.slice_before)) and np.array(model_total_implied_variance < self.slice_before).any(): # value +=(np.count_nonzero(~np.array(model_total_implied_variance < self.slice_before))*100) # # value = 1e6 # # if bool(len(self.slice_after)) and np.array(model_total_implied_variance > self.slice_after).any(): # value += float(np.count_nonzero(~np.array(model_total_implied_variance > self.slice_after)) * 100) # # value = 1e6 # if np.isnan(value): # value = 1e6 value = float(value) return value
def __init__(self, objfunc, var_bounds, individual_size, max_iter, max_or_min, **kwargs): super().__init__(objfunc) self.max_iter = max_iter # 定义个体 / 种群 self.individual = BinaryIndividual(ranges=var_bounds, eps=0.001) self.population = Population(indv_template=self.individual, size=individual_size).init() # Create genetic operators. # selection = RouletteWheelSelection() selection = TournamentSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitBigMutation(pm=0.1, pbm=0.55, alpha=0.6) self.engine = GAEngine(population=self.population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) @self.engine.fitness_register def fitness(indv): """ 适应度函数: 注意这里默认为优化得到最小值 :param indv: :return: """ x = indv.solution if max_or_min == 'max': return objfunc(x, **kwargs) else: return -objfunc(x, **kwargs) @self.engine.analysis_register class ConsoleOutputAnalysis(OnTheFlyAnalysis): interval = 1 master_only = True def register_step(self, g, population, engine): best_indv = population.best_indv(engine.fitness) msg = 'Generation: {}, best fitness: {:.3f}'.format( g, engine.fitness(best_indv)) # self.logger.info(msg) def finalize(self, population, engine): best_indv = population.best_indv(engine.fitness) x = best_indv.solution y = engine.fitness(best_indv) msg = 'Optimal solution: ({}, {})'.format(x, y)
def test_mutate(self): ''' Make sure the individual can be mutated correctly. ''' indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001) population = Population(indv_template=indv_template, size=50).init() # Create genetic operators. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitBigMutation(pm=0.03, pbm=0.2, alpha=0.6) # Create genetic algorithm engine. engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation) @engine.fitness_register def fitness(indv): x, = indv.solution return x + 10 * sin(5 * x) + 7 * cos(4 * x) mutation.mutate(indv_template, engine)
from gaft.components import BinaryIndividual from gaft.components import Population from gaft.operators import RouletteWheelSelection from gaft.operators import UniformCrossover from gaft.operators import FlipBitMutation from gaft.analysis.fitness_store import FitnessStore from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis from .mpi import MPI_klasa mpi = MPI_klasa() individual_template = BinaryIndividual(ranges=[(-2, 2), (-2, 2)], eps=0.001) population = Population(individual_template=individual_template, size=50) population.init() selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.25) mutation = FlipBitMutation(pm=0.1) gen_algo = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) def fitness(indv): x, y = indv.solution
def generate(self): import random eps = 0.2 # equal to actions space resolution # range/eps pop_size = 2 cross_prob = 0.6 exchange_prob = 0.7 mutation_pob = 0.8 generation = 6 REWARDS = [] tmp_reward = [] tmp_policy = [] bad_p = [] good_p = [] turb = 0 try: # Agents should make use of 20 episodes in each training run, if making sequential decisions first_action = [] for a1 in [i / 10 for i in range(0, 11, 2)]: for a2 in [i / 10 for i in range(0, 11, 2)]: first_action.append([a1, a2]) # [0,0] is absolutely bad action first_action = first_action[1:] action_reward = [] for i in range(len(first_action)): ar = self.environment.evaluateAction(first_action[i]) self.environment.reset() action_reward.append(ar[1]) # get the best policy for first year best_action = first_action[action_reward.index(max(action_reward))] test_action = ([[i / 10 for i in range(0, 11, 2)], [i / 10 for i in range(0, 11, 2)]]) # 2. Define population indv_template = OrderIndividual( ranges=[(0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1)], eps=eps, actions=test_action, best_1=best_action) # low_bit and high_bit population = Population(indv_template=indv_template, size=pop_size) population.init() # Initialize population with individuals. # 3. Create genetic operators # Use built-in operators here. selection = LinearRankingSelection() crossover = UniformCrossover( pc=cross_prob, pe=exchange_prob) # PE = Gene exchange probability mutation = FlipBitMutation( pm=mutation_pob) # 0.1 todo The probability of mutation # 4. Create genetic algorithm engine to run optimization engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation) # 5. Define and register fitness function @engine.fitness_register def fitness(indv): p = [0 for _ in range(10)] p = indv.solution # encode policy = { '1': [p[0], p[1]], '2': [p[2], p[3]], '3': [p[4], p[5]], '4': [p[6], p[7]], '5': [p[8], p[9]] } reward = self.environment.evaluatePolicy(policy) tmp_reward.append(reward) tmp_policy.append(policy) return reward + uniform(-turb, turb) @engine.analysis_register class ConsoleOutput(OnTheFlyAnalysis): master_only = True interval = 1 def register_step(self, g, population, engine): best_indv = population.best_indv(engine.fitness) msg = 'Generation: {}, best fitness: {:.3f}'.format( g + 1, engine.fmax) REWARDS.append( max(tmp_reward[pop_size * (g - 0):pop_size * (g + 1)])) engine.logger.info(msg) engine.run(ng=generation) best_reward = max(tmp_reward) best_policy = tmp_policy[-pop_size] except (KeyboardInterrupt, SystemExit): print(exc_info()) return best_policy, best_reward
from gaft.operators import * from gaft.analysis.fitness_store import FitnessStore from gaft.analysis.console_output import ConsoleOutput from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis import os from math import sin, cos, pi, exp import numpy as np # Define Generation generation = 50 # Define Population and the Constraints population_size = 10 indv_template = BinaryIndividual(ranges=[(-3, 12.1), (4.1, 5.8)], eps=0.001) population = Population(indv_template=indv_template, size=population_size).init() # Define Genetic Operators ## Selection : RouletteWheelSelection, TournamentSelection, LinearRankingSelection, ExpotentialRankingSelection #selection = RouletteWheelSelection() selection = TournamentSelection() ## Crossover ### pc : probability of crossover(usually between 0.25 - 1.0) ### pe : gene exchange probability crossover = UniformCrossover(pc=0.8, pe=0.5) ## Mutate ### pm : The probability of mutation (usually between 0.001 ~ 0.1) ### pbm : The probability of big mutation, usually more than 5 times bigger than pm ### alpha : intensive factor
import math from gaft import GAEngine from gaft.components import Population # 人口 from gaft.operators import FlipBitMutation # 翻转突变 from gaft.operators import UniformCrossover # 均匀交叉 from gaft.components import BinaryIndividual # 二元个体 from gaft.operators import RouletteWheelSelection # 轮盘选择 from gaft.analysis.console_output import ConsoleOutput # 输出 # 定义编码 individual_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001) # 定义种群 _population = Population(indv_template=individual_template, size=20) # 种群初始化 _population.init() # 遗传操作 selection = RouletteWheelSelection() # 个体选择:轮盘赌 crossover = UniformCrossover(pc=0.8, pe=0.5) # 交叉算子:均匀交叉 mutation = FlipBitMutation(pm=0.1) # 变异算子:翻转突变 # 遗传算法引擎 _engine = GAEngine(population=_population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput]) # 适应度:目标 @_engine.fitness_register
file_name=config.ZALL_SOM_3D_MODEL_NAME) elif CASE.upper() == 'SEL': obsSOM, true_labels = utils.loadSOM( save_dir=config.OUTPUT_TRAINED_MODELS_PATH, file_name=config.ZSEL_SOM_3D_MODEL_NAME) setupEnv() # print(MODELS_VALUES.shape) # print(MODELS_DICT) indv_template = ProbabilisticIndividual(ranges=[ (0, 1) for _ in range(config.NB_MODELS) ], eps=0.001) population = Population(indv_template=indv_template, size=POPULATION_SIZE) population.init() selection = RouletteWheelSelection() crossover = UniformCrossover(pc=CROSSOVER_PROBABILITY, pe=GE_PROBABILITY) mutation = FlipBitMutation(pm=MUTATION_PROBABILITY) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) @engine.fitness_register def fitness(indv): global MODELS_VALUES global MODELS_DICT
# Distribute task on multi-gpu comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() GPU_ID = rank % args.gpu_nums node_name = MPI.Get_processor_name() # get the name of the node os.environ['CUDA_VISIBLE_DEVICES'] = str(GPU_ID) time.sleep(rank * 5) print("node name: {}, GPU_ID: {}".format(node_name, GPU_ID)) # define population indv_template = DecimalIndividual(ranges=[(1.0, 1.2), (0.15, 0.7), (0.9, 1.0), (0.05, 0.65)], eps=0.0001) population = Population(indv_template=indv_template, size=100) # zzp: Population size population.init() # Initialize population with individuals. # Create genetic operators selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) # Create genetic algorithm engine to run optimization engine = GAEngine(population=population, selection=selection, \ crossover=crossover, mutation=mutation, \ analysis=[FitnessStore, ConsoleOutput]) # create model net = models.__dict__[args.arch]() net = load_pretrain(net, args.resume)
def generate(): import random good_seed = int(sys.argv[1]) print('currrrrrrrrrrrrrrrrrrrrrrrrrrrrr', good_seed) envSeqDec = ChallengeProveEnvironment() # Initialise a New Challenge Environment to post entire policy #env = ChallengeEnvironment(experimentCount = 20000) eps = 0.1 # equal to actions space resolution # range/eps pop_size = 4 cross_prob = 0.6 exchange_prob = 0.7 mutation_pob = 0.8 generation = 4 REWARDS = [] NEW = [] POLICY = [] tmp_reward = [] tmp_policy = [] policy_450 = [] reward_generation = [] time = [] random.seed(good_seed) # best_action = ([0], [0.8, 1]) turb = 0 test_action = ([[i/10 for i in range(0, 11)],[i/10 for i in range(0,11)]]) # test_action = ([0, 0.1, 0.2, 0.3, 0.4, 0.5], [0.6, 0.7, 0.8, 0.9, 1]) # 2. Define population indv_template = OrderIndividual(ranges=[(0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1)], eps=eps, actions = test_action) # low_bit and high_bit population = Population(indv_template=indv_template, size = pop_size) population.init() # Initialize population with individuals. # 3. Create genetic operators # Use built-in operators here. #selection = RouletteWheelSelection() selection = TournamentSelection() crossover = UniformCrossover(pc=cross_prob, pe=exchange_prob) # PE = Gene exchange probability mutation = FlipBitMutation(pm=mutation_pob) # 0.1 todo The probability of mutation # 4. Create genetic algorithm engine to run optimization engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation,) # analysis=[FitnessStore]) # 5. Define and register fitness function @engine.fitness_register #@engine.dynamic_linear_scaling(target='max', ksi0=2, r=0.9) def fitness(indv): p = [0 for _ in range(10)] p = indv.solution # encode policy = {'1': [p[0], p[1]], '2': [p[2], p[3]], '3': [p[4], p[5]], '4': [p[6], p[7]], '5': [p[8], p[9]]} reward = envSeqDec.evaluatePolicy(policy) # Action in Year 1 only #print('Sequential Result : ', reward) tmp_reward.append(reward) reward_generation.append(reward) tmp_policy.append(policy) #print('Policy : ', policy) #print(policy_450,'**************************good solution***************') #print(policy_bad,'**************************bad solution***************') return reward + uniform(-turb, turb) @engine.analysis_register class ConsoleOutput(OnTheFlyAnalysis): master_only = True interval = 1 def register_step(self, g, population, engine): best_indv = population.best_indv(engine.fitness) msg = 'Generation: {}, best fitness: {:.3f}'.format(g + 1, engine.fmax) #best_reward = max(tmp_reward[g + pop_size * (generation - 1): g + pop_size * generation]) #print(pop_size * (g - 0), pop_size * (g + 1),'&&&&&&&&&&&&&&&&&&&&&&&&&&&&&') REWARDS.append(max(reward_generation[pop_size * (g - 0): pop_size * (g + 1)])) #best_policy = POLICY[tmp_reward.index(best_reward)] #POLICY.append(best_policy) engine.logger.info(msg) engine.run(ng = generation) # print(policy_450) x = list(range(len(REWARDS))) plt.plot(x, REWARDS) plt.title(f'Sequential Rewards {good_seed}') plt.savefig(f'./GA_trick/GA_seed_{good_seed}.jpg') # #plt.savefig(f'./res_geneticAlgorithm/Sequential_Rewards_eps:{eps}_popsize:{pop_size}_generation:{generation}_mutation_pob:{mutation_pob}_exchange_prob:{exchange_prob}_cross_prob:{cross_prob}.jpg') plt.show()
""" Find the global maximum for binary function: f(x) = y*sim(2*pi*x) + x*cos(2*pi*y) """ import math from gaft import GAEngine from gaft.components import BinaryIndividual from gaft.components import Population from gaft.operators import TournamentSelection from gaft.operators import UniformCrossover from gaft.operators import FlipBitBigMutation from gaft.analysis.fitness_store import FitnessStore from gaft.analysis.console_output import ConsoleOutput individual_template = BinaryIndividual(ranges=[(-2, 2), (-2, 2)], eps=0.001) population = Population(indv_template=individual_template, size=50).init() selection = TournamentSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitBigMutation(pm=0.1, pbm=0.55, alpha=0.6) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput, FitnessStore]) @engine.fitness_register def fitness(individual): x, y = individual.solution return y * math.sin(2 * math.pi * x) + x * math.cos(2 * math.pi * y)
''' @engine.fitness_register @engine.minimize def fitness(indv): x, = indv.solution return x + 10*sin(5*x) + 7*cos(4*x) ''' if __name__ == '__main__': # Create individual (use binary encoding) indv = BinaryIndividual(ranges=[(0, 10)], eps=0.001) # Create a population with 50 individuals population = Population(indv_template=indv, size=50).init() # Create genetic operators: selection, crossover, mutation # 1. Tournament selection selection = TournamentSelection() # 2. Uniform crossover # pc is the probabililty of crossover operation # pe is the exchange probabiltiy for each possible gene bit in chromsome crossover = UniformCrossover(pc=0.8, pe=0.5) # 3. Flip bit mutation # pm is the probability of mutation mutation = FlipBitMutation(pm=0.1) # Create an engine to run engine1 = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation,
precisao = str(s).split('.')[1] noves = re.search(r'(^9*)', precisao) n_nines.append(len(noves[0])) fig = plt.figure() ax = fig.add_subplot(111) ax.plot(geracoes, n_nines) plt.show() if __name__ == "__main__": individuo = BinaryIndividual(ranges=[(-100, 100), (-100, 100)], eps=0.000001) populacao = Population(indv_template=individuo, size=100) populacao.init() selecao = TournamentSelection() crossover = UniformCrossover(pc=0.65, pe=0.65) mutacao = FlipBitMutation(pm=0.008) engine = GAEngine(population=populacao, selection=selecao, crossover=crossover, mutation=mutacao, analysis=[FitnessStore, ConsoleOutput]) @engine.fitness_register def aptidao(ind): x, y = ind.solution
from math import sin from gaft import GAEngine from gaft.components import BinaryIndividual, Population from gaft.operators import RouletteWheelSelection, UniformCrossover, FlipBitMutation from gaft.analysis import ConsoleOutput # Analysis plugin base class. from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis indv_template = BinaryIndividual(ranges=[(0, 15)], eps=0.001) population = Population(indv_template=indv_template, size=50) population.init() # Initialize population with individuals. # Use built-in operators here. selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[ConsoleOutput]) @engine.fitness_register def fitness(indv): x, = indv.solution return (-3) * (x - 30)**2 * sin(x)
class optimize_ga: def __init__(self ,k ,total_implied_variance ,slice_before ,slice_after ,tau): self.k =k self.total_implied_variance =total_implied_variance self.slice_before =slice_before self.slice_after = slice_after self.tau = tau # Define population. indv_template = BinaryIndividual(ranges=[(1e-5, 20),(1e-5, 20),(1e-5, 20)], eps=0.001) self.population = Population(indv_template=indv_template, size=30).init() # Create genetic operators. selection = TournamentSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) # Create genetic algorithm engine. self.engine = GAEngine(population=self.population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore]) # Define fitness function. @self.engine.fitness_register @self.engine.minimize def fitness(indv): a, b, m, rho, sigma = indv.solution model_total_implied_variance=svi_raw(self.k,np.array([a, b, m, rho, sigma]),self.tau) value = norm(self.total_implied_variance - model_total_implied_variance,ord=2) # if bool(len(self.slice_before)) and np.array(model_total_implied_variance < self.slice_before).any(): # value +=(np.count_nonzero(~np.array(model_total_implied_variance < self.slice_before))*100) # # value = 1e6 # # if bool(len(self.slice_after)) and np.array(model_total_implied_variance > self.slice_after).any(): # value += float(np.count_nonzero(~np.array(model_total_implied_variance > self.slice_after)) * 100) # # value = 1e6 # if np.isnan(value): # value = 1e6 value = float(value) return value # Define on-the-fly analysis. # @self.engine.analysis_register # class ConsoleOutputAnalysis(OnTheFlyAnalysis): # interval = 1 # master_only = True # # def register_step(self, g, population, engine): # best_indv = population.best_indv(engine.fitness) # msg = 'Generation: {}, best fitness: {:.3f}'.format(g, engine.ori_fmax) # self.logger.info(msg) # # def finalize(self, population, engine): # best_indv = population.best_indv(engine.fitness) # x = best_indv.solution # y = engine.ori_fmax # msg = 'Optimal solution: ({}, {})'.format(x, y) # self.logger.info(msg) def optimize(self): self.engine.run(ng=500) return self.population.best_indv(self.engine.fitness).solution
from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis # Built-in best fitness analysis. from gaft.analysis.fitness_store import FitnessStore # Define population. # 先对你所指定的初始种群进行编码 indv_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001) ''' :param ranges: value ranges for all entries in solution. :type ranges: list of range tuples. e.g. [(0, 1), (-1, 1)] :param eps: decrete precisions for binary encoding, default is 0.001. :type eps: float or float list with the same length with ranges. ''' population = Population(indv_template=indv_template, size=30).init() # Create genetic operators. selection = TournamentSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) ''' Crossover operator with uniform crossover algorithm, see https://en.wikipedia.org/wiki/Crossover_(genetic_algorithm) :param pc: The probability of crossover (usaully between 0.25 ~ 1.0) :type pc: float in (0.0, 1.0] :param pe: Gene exchange probability. ''' mutation = FlipBitMutation(pm=0.1) # pm is the possibility of the mutation
from gaft import GAEngine from gaft.components import BinaryIndividual, Population from gaft.operators import RouletteWheelSelection, UniformCrossover, FlipBitMutation from gaft.plugin_interfaces.analysis import OnTheFlyAnalysis from gaft.analysis.fitness_store import FitnessStore # bibliotecas from math import cos, sin import matplotlib.pyplot as plt import numpy as np # Parametros para geração dos individuos individuo_template = BinaryIndividual(ranges=[(0, 10)], eps=0.001) # Parametros para inicializar a população population = Population(indv_template=individuo_template, size=50) # Parametros para inicializar a população population.init() # Parametros para execução dos operadores selection = RouletteWheelSelection() crossover = UniformCrossover(pc=0.8, pe=0.5) mutation = FlipBitMutation(pm=0.1) engine = GAEngine(population=population, selection=selection, crossover=crossover, mutation=mutation, analysis=[FitnessStore])