Update __init__.py, best.py, calfitValue.py, calobjValue.py, crossover.py,...

Update __init__.py, best.py, calfitValue.py, calobjValue.py, crossover.py, ga.py, geneEncoding.py, selection.py, mutation.py files

best.py

+# 0.0 coding:utf-8 0.0
+# 找出最优解和最优解的基因编码
+
+def best(pop, fit_value):
+    px = len(pop)
+    best_individual = []
+    best_fit = fit_value[0]
+    for i in range(1, px):
+        if(fit_value[i] > best_fit):
+            best_fit = fit_value[i]
+            best_individual = pop[i]
+    return best_individual, best_fit
+
+if __name__ == '__main__':
+    pass

calfitValue.py

+# 0.0 coding:utf-8 0.0
+
+# 淘汰（去除负值）
+
+
+def calfitValue(obj_value):
+    fit_value = []
+    c_min = 0
+    for i in range(len(obj_value)):
+        if(obj_value[i] + c_min > 0):
+            temp = c_min + obj_value[i]
+        else:
+            temp = 0.0
+        fit_value.append(temp)
+    return fit_value
+
+if __name__ == '__main__':
+    pass

calobjValue.py

+# 0.0 coding:utf-8 0.0
+# 解码并计算值
+
+import math
+
+
+def decodechrom(pop, chrom_length):
+    temp = []
+    for i in range(len(pop)):
+        t = 0
+        for j in range(chrom_length):
+            t += pop[i][j] * (math.pow(2, j))
+        temp.append(t)
+    return temp
+
+
+def calobjValue(pop, chrom_length, max_value, ):
+    picture_sizeList = [] # 图片大小列表
+
+    
+    		
+    picture_sizeList.append(1.41)
+    picture_sizeList.append(1.85)
+    picture_sizeList.append(2.69)
+    picture_sizeList.append(2.16)
+    picture_sizeList.append(1.77)
+    picture_sizeList.append(1.19)	
+    picture_sizeList.append(1.91)
+    picture_sizeList.append(1.19)
+    picture_sizeList.append(1.91)
+    picture_sizeList.append(2.16)
+    picture_sizeList.append(1.77)
+    #1.85	2.69	2.16	1.77	1.19	1.91																		
+    # 0，1权重
+    w0 = 0.05
+    w1 = 0.01
+    #temp1 = []
+    obj_value = []
+    sum0 = sum1 = 0
+    # temp1 = decodechrom(pop, chrom_length)
+    # for i in range(len(temp1)):
+    for i in range(len(pop)):
+        sum0 = sum1 = 0
+        # x = temp1[i] * max_value / (math.pow(2, chrom_length) - 1)
+        # obj_value.append(10 * math.sin(5 * x) + 7 * math.cos(4 * x))
+        for j in range(chrom_length):
+            if pop[i][j] == 0:
+                sum0 = sum0 + w0 * picture_sizeList[j] + 0.04
+            else:
+                sum1 = sum1 + w1 * picture_sizeList[j] + 0.08
+        obj_value.append(1/max(sum0,sum1))
+
+    return obj_value
+
+if __name__ == '__main__':
+    pass

crossover.py

+# 0.0 coding:utf-8 0.0
+# 交配
+
+import random
+
+
+def crossover(pop, pc):
+    pop_len = len(pop)
+    for i in range(pop_len - 1):
+        if(random.random() < pc):
+            cpoint = random.randint(0,len(pop[0]))
+            temp1 = []
+            temp2 = []
+            temp1.extend(pop[i][0:cpoint])
+            temp1.extend(pop[i+1][cpoint:len(pop[i])])
+            temp2.extend(pop[i+1][0:cpoint])
+            temp2.extend(pop[i][cpoint:len(pop[i])])
+            pop[i] = temp1
+            pop[i+1] = temp2
+
+if __name__ == '__main__':
+    pass

ga.py

+# 0.0 coding:utf-8 0.0
+
+import matplotlib.pyplot as plt
+import math
+
+from calobjValue import calobjValue
+#from calfitValue import calfitValue
+from selection import selection
+from crossover import crossover
+from mutation import mutation
+from best import best
+from geneEncoding import geneEncoding
+from time import *
+
+# 计算2进制序列代表的数值
+# def b2d(b, max_value, chrom_length):
+# 	t = 0
+# 	for j in range(len(b)):
+# 		t += b[j] * (math.pow(2, j))
+# 	t = t * max_value / (math.pow(2, chrom_length) - 1)
+# 	return t
+
+inum = 20           # 迭代次数
+pop_size = 15		# 种群数量
+max_value = 20      # 基因中允许出现的最大值
+chrom_length = 7		# 染色体长度
+pc = 0.6			# 交配概率
+pm = 0.0001           # 变异概率
+results = []		# 存储每一代的最优解，N个二元组
+fit_value = []		# 个体适应度
+fit_mean = []		# 平均适应度
+
+pop = geneEncoding(pop_size, chrom_length)
+start=time()
+for i in range(inum):
+	obj_value = calobjValue(pop, chrom_length, max_value)        # 个体评价
+	# fit_value = calfitValue(obj_value)      # 淘汰
+	best_individual, best_fit = best(pop, obj_value)		# 第一个存储最优的解, 第二个存储最优基因
+	# results.append([best_fit, b2d(best_individual, max_value, chrom_length)])
+	results.append([best_fit, best_individual])
+	selection(pop, obj_value)		# 新种群复制
+	crossover(pop, pc)		# 交配
+	mutation(pop, pm)       # 变异
+end=time()
+print(end-start)
+#print(len(results))
+#results = results[1:]
+#print(len(results))
+#print(inum)
+# results.sort(reverse=True)
+#print(results[-1])
+print(best_individual)
+print(results)
+#print(best_fit)
+#print(obj_value[1])
+
+
+# print (results)
+# print ("y = %f, x = %f" % (results[-1][0], results[-1][1]))
+
+X = []
+Y = []
+for i in range(inum):
+	X.append(i)
+	t = 1/results[i][0]
+	Y.append(t)
+
+plt.plot(X, Y)
+
+plt.show()

geneEncoding.py

+# 0.0 coding:utf-8 0.0
+import random
+
+
+def geneEncoding(pop_size, chrom_length):
+    pop = [[]]
+    for i in range(pop_size):
+        temp = []
+        for j in range(chrom_length):
+            temp.append(random.randint(0, 1))
+        pop.append(temp)
+
+    return pop[1:]
+
+
+if __name__ == '__main__':
+    pop_size = 10		# 种群数量
+    chrom_length = 10		# 染色体长度
+    pop = geneEncoding(pop_size, chrom_length)
+    print (pop)
+    print (len(pop))

mutation.py

+# 0.0 coding:utf-8 0.0
+# 基因突变
+
+import random
+
+
+def mutation(pop, pm):
+    px = len(pop)
+    py = len(pop[0])
+    
+    for i in range(px):
+        if(random.random() < pm):
+            mpoint = random.randint(0, py-1)
+            if(pop[i][mpoint] == 1):
+                pop[i][mpoint] = 0
+            else:
+                pop[i][mpoint] = 1
+
+if __name__ == '__main__':
+    pass

selection.py

+# 0.0 coding:utf-8 0.0
+# 选择
+
+import random
+
+
+def sum(fit_value):
+	total = 0
+	for i in range(len(fit_value)):
+		total += fit_value[i]
+	return total
+
+
+def cumsum(fit_value):
+	for i in range(len(fit_value)-2, -1, -1):
+		t = 0
+		j = 0
+		while(j <= i):
+			t += fit_value[j]
+			j += 1
+		fit_value[i] = t
+		fit_value[len(fit_value)-1] = 1
+	return fit_value
+
+
+def selection(pop, fit_value):
+	newfit_value = []
+	# 适应度总和
+	total_fit = sum(fit_value) 
+	for i in range(len(fit_value)):
+		newfit_value.append(fit_value[i] / total_fit)
+	# 计算累计概率
+	newfit_value=cumsum(newfit_value)
+	ms = []
+	pop_len = len(pop)
+	for i in range(pop_len):
+		ms.append(random.random())
+	ms.sort()
+	fitin = 0
+	newin = 0
+	newpop = pop
+	# 转轮盘选择法
+	while newin < pop_len:
+		if(ms[newin] < newfit_value[fitin]):
+			newpop[newin] = pop[fitin]
+			newin = newin + 1
+		else:
+			fitin = fitin + 1
+	
+	pop = newpop
+
+if __name__ == '__main__':
+    pass