def Mutation(ind) :
#making list of mutated breed
mutationList = []
#print('mutating')
for item in ind :
#Create copy of breed to edit
indTemp = copy.deepcopy(item)
#Iterating over each piece position
for i in range(len(item['Positions'])):
mutate = numpy.random.choice([True,False],p = [0.05,0.95]) #Randomizer to decide to mutate or not
if mutate or indTemp['Positions'].count(indTemp['Positions'][i]) > 1: #Mutate the piece position according to randomizer above OR if it has duplicate on the list
#Creating mutated position
tupExist = True
while tupExist :
t1 = random.SystemRandom().randint(0,7)
t2 = random.SystemRandom().randint(0,7)
tupExist = (t1,t2) in item['Positions'] or (t1,t2) in indTemp['Positions']
#Assigning the mutated positon
indTemp['Positions'][i] = (t1,t2)
if (i < len(item['White'])):
indTemp['White'][i].position = (t1,t2)
else:
indTemp['Black'][i-len(item['White'])].position= (t1,t2)
#Re-calculate cost
indTemp['Cost'] = countThreat1(indTemp['White'],indTemp['Positions']) + countThreat1(indTemp['Black'],indTemp['Positions']) - countThreat2(indTemp['White'], indTemp['Black'],indTemp['Positions'])
mutationList.append(indTemp.copy())
return mutationList
def simulatedAnnealing(takenPos, whiteList, blackList):
#Initialize minState
minState = {}
minState['Positions'] = copy.deepcopy(takenPos)
minState['White'] = copy.deepcopy(whiteList)
minState['Black'] = copy.deepcopy(blackList)
minState['Cost'] = countThreat1(whiteList, takenPos) + countThreat1(
blackList, takenPos) - countThreat2(whiteList, blackList, takenPos)
#Initialize temperature
temperature = 100
while (temperature > 0):
#Make a successor state
tempState = findBetterState(minState['Positions'], minState['White'],
minState['Black'])
#Compare with current state
if (tempState['Cost'] < minState['Cost']):
minState = copy.deepcopy(tempState)
else:
probFactor = numpy.exp(
(minState['Cost'] - tempState['Cost']) / temperature)
change = numpy.random.choice([True, False],
p=[probFactor, 1 - probFactor])
if (change):
minState = copy.deepcopy(tempState)
#decrease temperature
temperature = temperature - 1
#temperature <= 0
return minState
def hillClimbing(takenPos, whiteList, blackList):
# Initialize minState
minState = {}
minState['Positions'] = copy.deepcopy(takenPos)
minState['White'] = copy.deepcopy(whiteList)
minState['Black'] = copy.deepcopy(blackList)
minState['Cost'] = countThreat1(whiteList, takenPos) + countThreat1(
blackList, takenPos) - countThreat2(whiteList, blackList, takenPos)
# If there is better state, algorithm keep finding a better state
betterExist = True
while betterExist:
tempState = findBetterState(minState['Positions'], minState['White'],
minState['Black'])
if (tempState['Cost'] < minState['Cost']):
minState = tempState
else:
betterExist = False
return minState
def findBetterState(takenPos, whiteList, blackList):
# Initialize minState from
minState = {}
minState['Cost'] = countThreat1(whiteList, takenPos) + countThreat1(
blackList, takenPos) - countThreat2(whiteList, blackList, takenPos)
minState['White'] = whiteList
minState['Black'] = blackList
minState['Positions'] = takenPos
# Iteration in white pion list to find a better assignment
for x in range(0, len(whiteList)):
# Iterate a and b to find new position
for a in range(0, 8):
for b in range(0, 8):
if ((a, b) not in takenPos):
# Make new temporary variable of position and white pion list, with position that haven't been taken
tempPos = copy.deepcopy(takenPos)
tempWhite = copy.deepcopy(whiteList)
tempWhite[x].position = (a, b)
tempPos[x] = (a, b)
# Count threats from the new position
tempV = countThreat1(tempWhite, takenPos) + countThreat1(
blackList, takenPos) - countThreat2(
tempWhite, blackList, takenPos)
# If the threat cost is less than the current minState, assign new minState with the temporary variables
if (tempV < minState['Cost']):
minState['Cost'] = tempV
minState['Positions'] = tempPos.copy()
minState['White'] = tempWhite.copy()
takenPos = tempPos.copy()
whiteList = tempWhite.copy()
# Iteration in black pion list to find a better assignment
for x in range(0, len(blackList)):
# Iterate a and b to find new position
for a in range(0, 8):
for b in range(0, 8):
if ((a, b) not in takenPos):
# Make new temporary variable of position and black pion list, with position that haven't been taken
tempPos = copy.deepcopy(takenPos)
tempBlack = copy.deepcopy(blackList)
tempBlack[x].position = (a, b)
tempPos[x + len(whiteList)] = (a, b)
# Count threats from the new position
tempV = countThreat1(tempWhite, takenPos) + countThreat1(
tempBlack, takenPos) - countThreat2(
whiteList, tempBlack, takenPos)
# If the threat cost is less than the current minState, assign new minState with the temporary variables
if (tempV < minState['Cost']):
minState['Cost'] = tempV
minState['Positions'] = tempPos.copy()
minState['Black'] = tempBlack.copy()
return (minState)
def generatePopulation(takenPos,whiteList,blackList):
#Making Population Object
obj = {}
obj['Positions'] = takenPos
obj['White'] = whiteList
obj['Black'] = blackList
obj['Cost'] = countThreat1(whiteList,takenPos) + countThreat1(blackList,takenPos) - countThreat2(whiteList,blackList,takenPos)
population = [obj]
#Making new state
for i in range(31):
tempWhite = copy.deepcopy(whiteList)
tempBlack = copy.deepcopy(blackList)
tempPos = copy.deepcopy(takenPos)
#Making new White List
for j in range(0,len(whiteList)):
loc = (random.SystemRandom().randint(0,7),random.SystemRandom().randint(0,7))
while loc in tempPos:
loc = (random.SystemRandom().randint(0,7),random.SystemRandom().randint(0,7))
tempWhite[j].position = loc
tempPos[j] = loc
#Making new Black List
for j in range(0,len(blackList)):
loc = (random.SystemRandom().randint(0,7),random.SystemRandom().randint(0,7))
while loc in tempPos:
loc = (random.SystemRandom().randint(0,7),random.SystemRandom().randint(0,7))
tempBlack[j].position = loc
tempPos[j+len(whiteList)] = loc
#Completing new population and adding it to population list
obj['Positions'] = tempPos
obj['White'] = tempWhite
obj['Black'] = tempBlack
obj['Cost'] = countThreat1(tempWhite,takenPos) + countThreat1(tempBlack,takenPos) + countThreat2(tempWhite,tempBlack,takenPos)
population.append(obj.copy())
return population
def Crossover(ind) :
#Make list of new breed
newInd = []
for tup in ind:
#Making intialization of new breed
newInd1 = {}
newInd1["White"] = []
newInd1["Black"] = []
newInd1["Positions"] = []
newInd2 = {}
newInd2["White"] = []
newInd2["Black"] = []
newInd2["Positions"] = []
#Crossovering White Piece Postition
for i in range(0,len(tup[0]['White'])):
rn = random.SystemRandom().randint(0,1) #Randomizing which one of the parent to copy the position
if(rn == 0):
if (tup[0]['White'][i] not in newInd1['Positions']): #Validation to prevent duplicate
newInd1["White"].append(tup[0]['White'][i])
newInd1["Positions"].append(tup[0]['Positions'][i])
newInd2["White"].append(tup[1]['White'][i])
newInd2["Positions"].append(tup[1]['Positions'][i])
else:
newInd1["White"].append(tup[1]['White'][i])
newInd1["Positions"].append(tup[1]['Positions'][i])
newInd2["White"].append(tup[0]['White'][i])
newInd2["Positions"].append(tup[0]['Positions'][i])
else :
if (tup[1]['White'][i] not in newInd1['Positions']):
newInd1["White"].append(tup[1]['White'][i])
newInd1["Positions"].append(tup[1]['Positions'][i])
newInd2["White"].append(tup[0]['White'][i])
newInd2["Positions"].append(tup[0]['Positions'][i])
else:
newInd1["White"].append(tup[0]['White'][i])
newInd1["Positions"].append(tup[0]['Positions'][i])
newInd2["White"].append(tup[1]['White'][i])
newInd2["Positions"].append(tup[1]['Positions'][i])
#Crossovering Black Piece Postition
for i in range(0,len(tup[0]['Black'])):
rn = random.SystemRandom().randint(0,1)
if(rn == 0):
if (tup[0]['Black'][i] not in newInd1['Positions']):
newInd1["Black"].append(tup[0]['Black'][i])
newInd1["Positions"].append(tup[0]['Positions'][i+len(newInd1['White'])])
newInd2["Black"].append(tup[1]['Black'][i])
newInd2["Positions"].append(tup[1]['Positions'][i+len(newInd2['White'])])
else:
newInd1["Black"].append(tup[1]['Black'][i])
newInd1["Positions"].append(tup[1]['Positions'][i+len(newInd1['White'])])
newInd2["Black"].append(tup[0]['Black'][i])
newInd2["Positions"].append(tup[0]['Positions'][i+len(newInd2['White'])])
else :
if (tup[1]['Black'][i] not in newInd1['Positions']):
newInd1["Black"].append(tup[1]['Black'][i])
newInd1["Positions"].append(tup[1]['Positions'][i+len(newInd1['White'])])
newInd2["Black"].append(tup[0]['Black'][i])
newInd2["Positions"].append(tup[0]['Positions'][i+len(newInd2['White'])])
else:
newInd1["Black"].append(tup[0]['Black'][i])
newInd1["Positions"].append(tup[0]['Positions'][i+len(newInd1['White'])])
newInd2["Black"].append(tup[1]['Black'][i])
newInd2["Positions"].append(tup[1]['Positions'][i+len(newInd2['White'])])
#Calculating the new cost
newInd1['Cost'] = countThreat1(newInd1['White'],newInd1['Positions']) + countThreat1(newInd1['Black'],newInd1['Positions']) - countThreat2(newInd1['White'],newInd1['Black'],newInd1['Positions'])
newInd2['Cost'] = countThreat1(newInd2['White'],newInd2['Positions']) + countThreat1(newInd2['Black'],newInd2['Positions']) - countThreat2(newInd2['White'],newInd2['Black'],newInd2['Positions'])
newInd.append(newInd1)
newInd.append(newInd2)
return(newInd)