Python利用遗传算法解决八皇后问题

老师课上的任务,用遗传算法 来实现 求解八皇后问题!感兴趣的可以试一试

import random
import matplotlib.pyplot as plt
a=[]
b=[]
c=[]
d=[]
def initState(a):
    while len(a)<8:
        a.append(random.randint(1,8))
    return a
def chooseParent(a,b,c,d):
    parent=[]
    stack=[]
    stack.append(a)
    stack.append(b)
    stack.append(c)
    stack.append(d)
    fitnessValue=fitnessFunc(stack)
    sort=sorted(fitnessValue.items(),key=lambda e:e[1],reverse=True)
    if sort[0][0]=='a':
        firstFather=a
    elif sort[0][0]=='b':
        firstFather=b
    elif sort[0][0]=='c':
        firstFather=c
    else:firstFather=d

    parent.append(firstFather)

    if sort[1][0]=='a':
        firstMother=a
    elif sort[1][0]=='b':
        firstMother=b
    elif sort[1][0]=='c':
        firstMother=c
    else:firstMother=d

    parent.append(firstMother)

    secondFather=firstFather
    parent.append(secondFather)

    if sort[2][0]=='a':
        secondMother=a
    elif sort[2][0]=='b':
        secondMother=b
    elif sort[2][0]=='c':
        secondMother=c
    else:secondMother=d

    parent.append(secondMother)

    return parent


def fitnessFunc(myList):
    fitnessIndex=[28,28,28,28]
    for c in range(0,4):
        for i in range(0,7):
            for n in range(i+1,8):
                if myList[c][i]==myList[c][n]:
                    fitnessIndex[c]-=1
                if myList[c][i]+n-i==myList[c][n] or myList[c][i]-n+i==myList[c][n]:
                    fitnessIndex[c]-=1
    fitnessDict={'a':fitnessIndex[0],'b':fitnessIndex[1],'c':fitnessIndex[2],'d':fitnessIndex[3]}
    return fitnessDict



def crossOver(myList):
    newList=[]
    crossPoint=random.randint(1,7)
    ff=myList[0]
    fm=myList[1]
    sf=myList[2]
    sm=myList[3]

    fchild1=ff[0:crossPoint]
    fchild1.extend(fm[crossPoint:])

    fchild2=fm[0:crossPoint]
    fchild2.extend(ff[crossPoint:])

    schild1=sf[0:crossPoint]
    schild1.extend(sm[crossPoint:])

    schild2=sm[0:crossPoint]
    schild2.extend(sf[crossPoint:])

    newList.append(fchild1)
    newList.append(fchild2)
    newList.append(schild1)
    newList.append(schild2)
    return newList
def mutationPoint():
    mPoint=random.randint(0,8)
    return mPoint
def mutationNumber():
    mNumber=random.randint(1,8)
    return mNumber
def mutation(myList):

    childMutationList=[]
    fc1_mPoint=mutationPoint()
    fc1_mNumber=mutationNumber()
    fchild1=myList[0]
    if fc1_mPoint==0:
        fchild1_m=fchild1
    else:
        fchild1_m=fchild1
        fchild1_m[fc1_mPoint-1]=fc1_mNumber

    fc2_mPoint=mutationPoint()
    fc2_mNumber=mutationNumber()
    fchild2=myList[1]
    if fc2_mPoint==0:
        fchild2_m=fchild2
    else:
        fchild2_m=fchild2
        fchild2_m[fc2_mPoint-1]=fc2_mNumber

    sc1_mPoint=mutationPoint()
    sc1_mNumber=mutationNumber()
    schild1=myList[2]
    if sc1_mPoint==0:
        schild1_m=schild1
    else:
        schild1_m=schild1
        schild1_m[sc1_mPoint-1]=sc1_mNumber

    sc2_mPoint=mutationPoint()
    sc2_mNumber=mutationNumber()
    schild2=myList[3]
    if sc2_mPoint==0:
        schild2_m=schild2
    else:
        schild2_m=schild2
        schild2_m[sc2_mPoint-1]=sc2_mNumber

    childMutationList.append(fchild1_m)
    childMutationList.append(fchild2_m)
    childMutationList.append(schild1_m)
    childMutationList.append(schild2_m)
    return childMutationList



print(initState(a))
print(initState(b))
print(initState(c))
print(initState(d))
parent=chooseParent(a,b,c,d)
# print(parent)
answer=[]
asumeCount=0
while(True):
    crossParent=crossOver(parent)
    mutationParent=mutation(crossParent)
    newFitDict=fitnessFunc(mutationParent)
    asumeCount+=1
    if newFitDict['a']==28 or newFitDict['b']==28 or newFitDict['c']==28 or newFitDict['d']==28:
        if newFitDict['a']==28:
            answer.append(mutationParent[0])
        if newFitDict['b']==28:
            answer.append(mutationParent[1])
        if newFitDict['c']==28:
            answer.append(mutationParent[2])
        if newFitDict['d']==28:
            answer.append(mutationParent[3])
        print("I find it! The answer is {0}".format(answer))
        print("I did {0} cycle to find it".format(asumeCount))
        for i in answer:
            pointA=i.index(1)+1
            pointB=i.index(2)+1
            pointC=i.index(3)+1
            pointD=i.index(4)+1
            pointE=i.index(5)+1
            pointF=i.index(6)+1
            pointG=i.index(7)+1
            pointH=i.index(8)+1
            x=[1,2,3,4,5,6,7,8]
            y=[pointA,pointB,pointC,pointD,pointE,pointF,pointG,pointH]
            plt.axis([0,9,0,9])
            plt.plot(x,y, '*')
            plt.show()

        break
    else:parent=mutationParent

    原文作者:遗传算法
    原文地址: https://blog.csdn.net/lsdnh521/article/details/48850483
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
点赞