def test_population(params=None, gen_no=0):
if params is None:
from utils import StatusUpdateTool
params = StatusUpdateTool.get_init_params()
gen_no = 20
pop = Population(params, gen_no)
pop.initialize()
print(pop)
def test_individual(params=None, indi_no=2):
if params is None:
from utils import StatusUpdateTool
params = StatusUpdateTool.get_init_params()
ind = Individual(params, indi_no)
ind.initialize()
print(ind)
print(ind.uuid())
self.fitness_evaluate()
Log.info('EVOLVE[%d-gen]-Finish the evaluation' % (gen_no))
gen_no += 1
for curr_gen in range(gen_no, max_gen):
self.params['gen_no'] = curr_gen
# step 3
# .1 交叉变异 .2 估计适应度 .3 筛选
Log.info('EVOLVE[%d-gen]-Begin to crossover and mutation' %
(curr_gen))
self.crossover_and_mutation()
Log.info('EVOLVE[%d-gen]-Finish crossover and mutation' %
(curr_gen))
Log.info('EVOLVE[%d-gen]-Begin to evaluate the fitness' %
(curr_gen))
self.fitness_evaluate()
Log.info('EVOLVE[%d-gen]-Finish the evaluation' % (curr_gen))
self.environment_selection()
Log.info('EVOLVE[%d-gen]-Finish the environment selection' %
(curr_gen))
StatusUpdateTool.end_evolution()
if __name__ == '__main__':
params = StatusUpdateTool.get_init_params()
evoCNN = EvolveCNN(params)
evoCNN.do_work(max_gen=5)