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)
from gaft.operators import UniformCrossover
from gaft.operators import FlipBitBigMutation
# Built-in best fitness analysis.
from gaft.analysis.fitness_store import FitnessStore
from gaft.analysis.console_output import ConsoleOutput
# Define population.
indv_template = BinaryIndividual(ranges=[(-2, 2), (-2, 2)], eps=0.001)
population = Population(indv_template=indv_template, size=50).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)
# 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=[ConsoleOutput, FitnessStore])
# Define fitness function.
@engine.fitness_register
def fitness(indv):
x, y = indv.solution
return y * sin(2 * pi * x) + x * cos(2 * pi * y)
# Built-in best fitness analysis.
from gaft.analysis.fitness_store import FitnessStore
from gaft.analysis.console_output import ConsoleOutput
# Define population.
indv_template = BinaryIndividual(ranges=[XB, YB], eps=EPS)
population = Population(indv_template=indv_template,
size=POPULATION_SIZE).init()
# Create genetic operators.
selection = SELECTION
crossover = UniformCrossover(pc=PC, pe=PE)
mutation = FlipBitBigMutation(pm=PM, pbm=PBM, alpha=ALPHA)
# 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=[ConsoleOutput, FitnessStore])
# Define fitness function.
@engine.fitness_register
@engine.minimize
def fitness(indv):
x, y = indv.solution
