def getNewChild(self):
logging.debug('select 2 parent lives')
parent1 = self.selection()
parent2 = self.selection()
while self.hammingDist(parent1._gene, parent2._gene) == 0:
parent2 = self.selection()
pass
variableCount = len(self._boundaryList)
encodeLength = int(self._geneLength / variableCount)
# logging.debug('parent1: \n%s' % self.decodedOneGene(parent1._gene))
# logging.debug('parent2: \n%s' % parent1._gene.reshape((variableCount, encodeLength)))
# logging.debug('parent2: \n%s' % self.decodedOneGene(parent2._gene))
# logging.debug('parent2: \n%s' % parent2._gene.reshape((variableCount, encodeLength)))
logging.debug(
'hamming distance between parent1 and parent2: %d' % self.hammingDist(parent1._gene, parent2._gene))
# 按概率交叉
rate = random.random()
if rate < self._crossoverRate:
logging.debug('crossover')
# 交叉
gene1, gene2 = self.crossover(parent1, parent2)
logging.debug(
'hamming distance between gene1 and gene2 after crossover: %d' % self.hammingDist(gene1, gene2))
else:
gene1, gene2 = parent1._gene, parent2._gene
# 按概率突变
rate = random.random()
if rate < self._mutationRate:
logging.debug('mutation')
gene1, gene2 = self.mutation(gene1), self.mutation(gene2)
logging.debug(
'hamming distance between gene1 and gene2 after mutation: %d' % self.hammingDist(gene1, gene2))
# 计算子代适应度
life1 = Life(gene1)
life2 = Life(gene2)
life1._fitness = self._fitnessClass.fitnessFunc(self.decodedOneGene(gene1))
life2._fitness = self._fitnessClass.fitnessFunc(self.decodedOneGene(gene2))
return life1, life2
def initPopulation(self):
logging.debug('init population')
self._population = []
if self._binaryEncode: # 将变量进行二进制编码
if self._boundaryList is None:
raise ValueError("boundaryList must be configured!")
# 获取编码长度列表
self._encodeLengths = self.getEncodedLengths()
# 基因长度为每个变量编码长度之和
self._geneLength = np.sum(self._encodeLengths)
# 随机化初始种群为0
for i in range(self._populationSize):
# 随机生成基因
gene = np.random.randint(0, 2, self._geneLength)
# 生成个体，并计算适应度
life = Life(gene)
life._fitness = self._fitnessClass.fitnessFunc(self.decodedOneGene(gene))
# 把生成个体添加至种群集合里
self._population.append(life)
else: # 编码方式为[0, 1, 2, ..., self._geneLength-1]
if self._geneLength is None:
raise ValueError("geneLength must be configured!")
for i in range(self._populationSize):
gene = np.array(range(self._geneLength))
# 将基因乱序
random.shuffle(gene)
# 生成个体，并计算适应度
life = Life(gene)
life._fitness = self._fitnessClass.fitnessFunc(gene)
# 把生成个体添加至种群集合里
self._population.append(life)
# 根据适应度值对种群的个体降序排列，并记录最佳个体和最佳适应度
self._population = self.sortPopulation(self._population)
self._bestLife = self._population[0]
self._bestFitnessHistory.append(self._bestLife._fitness)
# 计算总适应度
self._totalFitness = self.calTotalFitness(self._population)
self._totalFitnessHistory.append(self._totalFitness)
