为了应对更加复杂的问题，不同的编码方式的问题，对此进行代码讲解，个人觉得这个遗传算法库做的很好，上手比较快，适用性很强，强烈推荐，用的很顺手。
示例说明：
a = [0~30连续数] * _num
b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列
c = [0~30离散数（整数）] * _num
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import geatpy as ea import numpy as np # 示例说明： # a = [0~30连续数] * _num # b = [1 ~ num num个数排列排序] 即数列 1~num 不重复排列 # c = [0~30离散数（整数）] * _num def func(a, b, c): return np.sin(np.pi * a * b) * np.cos(np.pi * c * b) * np.tan(np.pi * a * b * c) # 初始化文件num大小 _num = 10 # 自定义 GA class My_nsga(ea.Problem): def __init__(self): global _num name = 'GEATPY Facilitate Learning' # 只优化func一个变量 M = 1 # 最大化变量 maxormins = [-1] * M # 一共有 a.size + b.size + c.size 个数 Dim = _num + _num + _num # a 连续， b and c 离散 varTypes = [0] * _num + [1] * _num + [1] * _num # a ∈ [0,30], b ∈ [1,_num], c ∈ [0,30] lb = [0] * _num + [1] * _num + [0] * _num ub = [30] * _num + [_num] * _num + [30] * _num # 下限可取 lbin = [1] * Dim # 上限可取 ubin = [1] * Dim # 保存最大数据 self.max_ans = 0 # epoch self.epoch = 0 # super ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin) # 目标函数即神经网络返回值 def evalVars(self, Vars): # init ans ans = np.zeros(len(Vars), dtype=float).reshape(len(Vars), 1) for i in range(len(Vars)): # 一定要确定区域选择是否正确 a = Vars[i][:_num] # 可以选择int类型防止特定错误，因为正常输出时为float，具体问题可能会出错 b = list(map(int, Vars[i][_num: _num + _num])) c = Vars[i][-_num:] score = func(a, b, c) ans[i] = score.max() self.max_ans = (now_max := ans.max()) print(f"Epoch: {self.epoch + 1}, Epoch Max: {now_max}, Global Max: {self.max_ans}") self.epoch += 1 return ans # 运行 GA def Run_nsga(ndind=10, maxgen=100, *args, **kwargs): global _num # init problem problem = My_nsga() # 染色体编码方式为[格雷编码，排列编码，格雷编码] encodings = ["RI", "P", "RI"] # 设置具体field，一定要多加这一部，geatpy还没有具体到能直接输入encoding: List[str]，需要进行转换才能使用 # 一定要注意数组范围，报错的话一定要仔细检查！！！ field1 = ea.crtfld(encodings[0], problem.varTypes[:_num], problem.ranges[:, :_num], problem.borders[:, :_num]) field2 = ea.crtfld(encodings[1], problem.varTypes[_num: _num + _num], problem.ranges[:, _num: _num + _num], problem.borders[:, _num: _num + _num]) field3 = ea.crtfld(encodings[2], problem.varTypes[-_num:], problem.ranges[:, -_num:], problem.borders[:, -_num:]) # 合并field fields = [field1, field2, field3] # 代入模板 myAlgorithm = ea.soea_psy_EGA_templet(problem, ea.PsyPopulation(Encodings=encodings, Fields=fields, NIND=ndind), MAXGEN=maxgen, logTras=0) # 是否画图 myAlgorithm.drawing = 0 # 进行优化 res = ea.optimize(myAlgorithm, seed=1, verbose=False, drawing=0, outputMsg=True, drawLog=False, saveFlag=False, dirName='result') return res['Vars'][0] if __name__ == "__main__": Run_nsga(50, 1000) |
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