【实验】遗传算法的超参数优化

# wine数据集分类结果 ## Grid Search > best parameters: {'algorithm': 'SAMME', 'learning_rate': 0.3593813663804626, 'n_estimators': 60} > best score: 0.9720634920634922 > Time Elapse: 113.00295925140381 ## Grid Search Based GA > Best individual is: {'n_estimators': 100, 'learning_rate': 0.3593813663804626, 'algorithm': 'SAMME'} > with fitness: 0.9719101123595506 > Time Elapsed = 46.56487488746643 ## GA ### 结果： 1. > ```python > POPULATION_SIZE = 10 > P_CROSSOVER = 0.9 # probability for crossover > P_MUTATION = 0.5 # probability for mutating an individual > MAX_GENERATIONS = 10 > HALL_OF_FAME_SIZE = 5 > CROWDING_FACTOR = 20.0 # crowding factor for crossover and mutation > ``` > Best solution is: > params = (67, 0.34684801135281196, 'SAMME') > Accuracy = 0.97206 > >  2. > POPULATION_SIZE = 10 > P_CROSSOVER = 0.9 # probability for crossover > P_MUTATION = 0.5 # probability for mutating an individual > MAX_GENERATIONS = 10 > HALL_OF_FAME_SIZE = 5 > CROWDING_FACTOR = 20.0 # crowding factor for crossover and mutation > Best solution is: > params = (67, 0.34684801135281196, 'SAMME') > Accuracy = 0.97206 > >  ## PSO ### 算法大致步骤： * 初始化 * 更新 可参考的blog：[(87条消息) 智能优化算法——粒子群算法原理与仿真程序_子木呀的博客-CSDN博客_粒子群优化算法原理](https://blog.csdn.net/qq_41687938/article/details/122641718) ### 代码实现设置的条件： ```python MAX_Generation = 10 #迭代次数 Population = 10 #种群数量 dimension = 3 v_low = -1 v_high = 1 pso = PSO(dimension, MAX_Generation, Population, BOUNDS_LOW, BOUNDS_HIGH, v_low, v_high) #实现的class ``` ### 更新步骤的核心代码: 网上搜一下，其中w自身权重系数（记不清了），c1是个体学习系数，c2是群落学习系数 ```python c1 = 2.0 # 学习因子 c2 = 2.0 w = 0.8 # 更新速度(核心公式) self.v[i] = w * self.v[i] + c1 * random.uniform(0, 1) * ( self.p_best[i] - self.x[i]) + c2 * random.uniform(0, 1) * (self.g_best - self.x[i]) # 速度限制 for j in range(self.dimension): if self.v[i][j] < self.v_low: self.v[i][j] = self.v_low if self.v[i][j] > self.v_high: self.v[i][j] = self.v_high # 更新位置 self.x[i] = self.x[i] + self.v[i] # 位置限制 for j in range(self.dimension): if self.x[i][j] < self.bound[0][j]: self.x[i][j] = self.bound[0][j] if self.x[i][j] > self.bound[1][j]: self.x[i][j] = self.bound[1][j] ``` 大致的意思就是通过公式更新速度和位置，同时对更新后的速度与位置进行修正，因为更新的位置一定是在一定的范围之内的。 ### 结果： 1. > MAX_Generation = 10 #迭代次数 > Population = 10 #种群数量 > > v_low = -1 > > v_high = 1 > > c1 = 2.0 # 学习因子 > c2 = 2.0 > w = 0.8 > > --------------------------- > n_estimators'=97.59544738, 'learning_rate'=0.93788895, 'algorithm'=SAMME > 0.9830158730158731 > 当前的最佳适应度：0.9830158730158731 > time cost: 175.3606903553009 > >  2. > ################ 改变迭代次数和种群大小####################### > > MAX_Generation = 50 #迭代次数 > Population = 20 #种群数量 > > v_low = -1 > > v_high = 1 > > c1 = 2.0 # 学习因子 > c2 = 2.0 > w = 0.8 > 当前最佳位置：[22.63664273 0.7580015 0.39425009] > 0.9831746031746033 > 当前的最佳适应度：0.9831746031746033 > time cost: 1097.9579124450684 s > >  3. > ################ 改变速度大小####################### > > MAX_Generation = 50 #迭代次数 > Population = 20 #种群数量 > > v_low = -0.5 > > v_high = 0.5 > > c1 = 2.0 # 学习因子 > c2 = 2.0 > w = 0.8 > > > 当前最佳位置：[7.49302006e+01 6.20588351e-01 2.87210702e-02] > 0.9831746031746033 > 当前的最佳适应度：0.9831746031746033 > time cost: 1510.600219488144 s 4. > ################ 改变速度范围####################### > > MAX_Generation = 50 #迭代次数 > Population = 20 #种群数量 > > v_low = [-10, -0.1, -0.5] > > v_high = [10, 0.1, 0.5] > > c1 = 2.0 # 学习因子 > c2 = 2.0 > w = 0.8 > 当前最佳位置：[17.96950042 0.88984003 0. ] > 0.9885714285714287 > 当前的最佳适应度：0.9885714285714287 > time cost: 1343.2985899448395 s > >  5. > 改变初始化 > 当前最佳位置：[58.67594087 0.40936569 0. ] > 0.9776190476190475 > 当前的最佳适应度：0.9776190476190475 > time cost: 2636.623948097229 s > >