n = 5
lb = -5.12 * np.ones((1, n))
ub = 5.12 * np.ones((1, n))
ranges = np.vstack([lb, ub]) # 生成自变量的范围矩阵
lbin = np.ones((1, n))
ubin = np.ones((1, n))
borders = np.vstack([lbin, ubin]) # 生成自变量的边界矩阵（1表示变量的区间是闭区间）
precisions = [
1
] * n # 根据crtfld的函数特性，这里需要设置精度为任意正值，否则在生成区域描述器时会默认为整数编码，并对变量范围作出一定调整
FieldDR = ga.crtfld(ranges, borders, precisions) # 生成区域描述器
# 调用编程模板
[pop_trace, var_trace,
times] = ga.sga_new_real_templet(AIM_M,
'aimfuc',
None,
None,
FieldDR,
problem='R',
maxormin=1,
MAXGEN=1000,
NIND=100,
SUBPOP=1,
GGAP=0.9,
selectStyle='sus',
recombinStyle='xovdprs',
recopt=None,
pm=None,
distribute=True,
drawing=1)
调用算法模板时可以设置drawing=2，此时算法模板将在种群进化过程中绘制动画，但注意执行前要在Python控制台执行命令matplotlib qt5。
"""
import numpy as np
import geatpy as ga
# 获取函数接口地址
AIM_M = __import__('aimfuc')
# 变量设置
ranges = np.vstack([np.zeros((1, 4)), np.ones((1, 4))]) # 生成自变量的范围矩阵
borders = np.vstack([np.ones((1, 4)), np.ones((1, 4))]) # 生成自变量的边界矩阵
FieldDR = ga.crtfld(ranges, borders) # 生成区域描述器
# 调用编程模板(设置problem = 'I'处理离散型变量问题，详见该算法模板的源代码)
[pop_trace, var_trace, times] = ga.sga_new_real_templet(AIM_M,
'aimfuc',
None,
None,
FieldDR,
problem='I',
maxormin=-1,
MAXGEN=50,
NIND=10,
SUBPOP=1,
GGAP=0.9,
selectStyle='sus',
recombinStyle='xovdp',
recopt=0.9,
pm=0.1,
distribute=True,
drawing=1)
if __name__ == "__main__":
AIM_M = __import__('main') # 获取函数接口所在文件的地址
# 变量设置
ranges = np.vstack(
[32 * np.ones((1, len(words))), 122 * np.ones(
(1, len(words)))]) # 生成自变量的范围矩阵
borders = np.vstack(
[np.ones((1, len(words))), 122 * np.ones(
(1, len(words)))]) # 生成自变量的边界矩阵
FieldDR = ga.crtfld(ranges, borders) # 生成区域描述器
# 调用编程模板
[pop_trace, var_trace,
times] = ga.sga_new_real_templet(AIM_M,
'aim',
None,
None,
FieldDR,
problem='I',
maxormin=1,
MAXGEN=2000,
NIND=50,
SUBPOP=1,
GGAP=0.9,
selectStyle='sus',
recombinStyle='xovdp',
recopt=0.9,
pm=0.1,
drawing=1)
# 输出结果
for num in var_trace[np.argmin(pop_trace[:, 1]), :]:
print(chr(int(num)), end='')