def crossover(self, crossover_rate):
temp_population = []
new_population = []
for i in range(len(self.population)):
temp_population.append(self.selection())
for i in range(0, len(temp_population) - 1, 2):
if uniform(0, 1) < crossover_rate:
crossover_point = randint(1, self.n_genes)
temp1 = temp_population[i].chromo_numb[:crossover_point]
temp2 = temp_population[i + 1].chromo_numb[crossover_point:]
son_chromo_numb1 = temp1 + temp2
temp1 = temp_population[i + 1].chromo_numb[:crossover_point]
temp2 = temp_population[i].chromo_numb[crossover_point:]
son_chromo_numb2 = temp1 + temp2
son_chromo1 = Chromosome(self.n_genes)
son_chromo1.generate(son_chromo_numb1)
son_chromo1.numbToExpr(self.max_loops, self.grammar)
son_chromo1.calculateFitness(self.data_train)
son_chromo2 = Chromosome(self.n_genes)
son_chromo2.generate(son_chromo_numb2)
son_chromo2.numbToExpr(self.max_loops, self.grammar)
son_chromo2.calculateFitness(self.data_train)
new_population.append(son_chromo1)
new_population.append(son_chromo2)
else:
new_population.append(temp_population[i])
new_population.append(temp_population[i + 1])
if len(temp_population) % 2 == 1:
new_population.append(temp_population[-1])
self.population = new_population.copy()
def generate(self):
while len(self.population) != self.pop_size:
chromo = Chromosome(self.n_genes)
chromo.generate()
chromo.numbToExpr(self.max_loops, self.grammar)
chromo.calculateFitness(self.data_train)
#if chromo.fitness != float('infinity') and len(chromo.chromo_expr)>1:
self.population.append(chromo)