def test_run(self):
'''
Make sure GA engine can run correctly.
'''
indv_template = GAIndividual(ranges=[(0, 10)],
encoding='binary',
eps=0.001)
population = GAPopulation(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
def fitness(indv):
x, = indv.variants
return x + 10 * sin(5 * x) + 7 * cos(4 * x)
engine.run(50)
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 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 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 __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
'''
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
# Create genetic algorithm engine.
engine = GAEngine(population=population, selection=selection,
crossover=crossover, mutation=mutation,
analysis=[FitnessStore])
# Define fitness function.
@engine.fitness_register
def fitness(indv):
x, = indv.solution
return x + 10*sin(5*x) + 7*cos(4*x)
# Define on-the-fly analysis.
@engine.analysis_register
class ConsoleOutputAnalysis(OnTheFlyAnalysis):
#str_P = 20
#str_PC = 0.8
#str_PM = 0.1
#str_ng = 2
# Define population.
indv_template = GAIndividual(ranges=[(str_THF_LB, str_THF_HB),
(str_TOL_LB, str_TOL_HB)],
encoding='binary',
eps=0.001)
population = GAPopulation(indv_template=indv_template, size=str_P).init()
# Create genetic operators.
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=str_PC, pe=0.5)
mutation = FlipBitMutation(pm=str_PM)
# Create genetic algorithm engine.
# Here we pass all built-in analysis to engine constructor.
engine = GAEngine(population=population,
selection=selection,
crossover=crossover,
mutation=mutation,
analysis=[ConsoleOutputAnalysis, FitnessStoreAnalysis])
# Define fitness function.
@engine.fitness_register
def fitness(indv):
# Active HYSYS Model
hyApp_Fitness = win32.Dispatch("HYSYS.Application")
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.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
# @_engine.minimize
def fitness(individual):
x, = individual.solution
return 0.01 * x + 10 * math.sin(5 * x) + 7 * math.cos(4 * x)
if __name__ == '__main__':
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
global CASE
global true_labels
solution_weights = indv.solution
for i in range(popsize):
indv_list.append(indv_template.clone())
population = GAPopulation(indv_template=indv_template, size=popsize).init(indv_list)
# Create genetic operators.
# selection = TournamentSelection()#RouletteWheelSelection()
if tselect:
print('Tournament selection')
selection = TournamentSelection()
else:
print('RouletteWheelSelection')
selection = RouletteWheelSelection()
crossover = UniformCrossover(pc=pc, pe=pe)
mutation = FlipBitMutation(pm=pm)
# Create genetic algorithm engine.
engine = GAEngine(population=population, selection=selection,
crossover=crossover, mutation=mutation,
analysis=[FitnessStore])
@engine.fitness_register
def fitness(indv):
# print('x variants',indv.variants,type(indv.variants),len(indv.variants))
x_decode = indv.chromsome
assert (len(x_decode)==n)
state = decode_sequence(x_decode)
power = power_by_mtt_fastgraph(state, graph)
POLICY = []
tmp_reward = []
tmp_policy = []
random.seed(c)
turb = 5
# 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]]}
reward = envSeqDec.evaluatePolicy(policy) # Action in Year 1 only
print('Sequential Result : ', reward)
tmp_reward.append(reward)
tmp_policy.append(policy)
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
return 0.5 - ((sin(sqrt(x**2 + y**2))**2 - 0.5) / (1 + 0.001 *
(x**2 + y**2))**2)
engine.run(ng=40)
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()