def onePointCrossover(self, parent1, parent2, crossoverRate):
""" Create two new child candidates by crossing over parent genes.
Parent genes are splitted by one point and then concatenate to generate child genes
Parameters:
- parent1 (Candidate): First parent to crossover
- parent2 (Candidate): Second parent to crossover
- crossoverRate (float): Ratio defines if these parents are crossover or not
Return:
Tuple of two child generate from the crossover process
"""
child1 = Candidate()
child2 = Candidate()
# Make a copy of the parent genes.
grid1 = npCopy(parent1.gene)
grid2 = npCopy(parent2.gene)
gridSize = len(parent1.gene)
r = random.random()
if r < crossoverRate:
# Get a ranom crossover point to split parent genes
crossPoint = random.randint(1, gridSize - 2)
child1.gene = npConcatenate(
(grid1[:crossPoint], grid2[crossPoint:]), axis=0)
child2.gene = npConcatenate(
(grid2[:crossPoint], grid1[crossPoint:]), axis=0)
else:
child1.gene = grid1
child2.gene = grid2
return child1, child2
def choiceCrossover(self, parent1, parent2, crossoverRate):
""" Create two new child candidates by crossing over parent genes.
The child will randomly choose each sub grid from first parent or second parent
Parameters:
- parent1 (Candidate): First parent to crossover
- parent2 (Candidate): Second parent to crossover
- crossoverRate (float): Ratio defines if these parents are crossover or not
Return:
Tuple of two child generate from the crossover process
"""
child1 = Candidate()
child2 = Candidate()
# Make a copy of the parent genes.
grid1 = parent1.gene
grid2 = parent2.gene
gridSize = len(parent1.gene)
r = random.random()
if r < crossoverRate:
for i in range(gridSize):
# Randomly select sub-block from two parents to generate new child
blocks = [grid1[i], grid2[i]]
child1.gene[i] = npCopy(random.choice(blocks))
child2.gene[i] = npCopy(random.choice(blocks))
else:
child1.gene = npCopy(grid1)
child2.gene = npCopy(grid2)
return child1, child2
def uniformCrossover(self, parent1, parent2, crossoverRate):
""" Create two new child candidates by crossing over parent genes.
Parent genes will swap 2 consecutive sub-blocks to generate child genes
Parameters:
- parent1 (Candidate): First parent to crossover
- parent2 (Candidate): Second parent to crossover
- crossoverRate (float): Ratio defines if these parents are crossover or not
Return:
Tuple of two child generate from the crossover process
"""
child1 = Candidate()
child2 = Candidate()
# Make a copy of the parent genes.
grid1 = npCopy(parent1.gene)
grid2 = npCopy(parent2.gene)
gridSize = len(parent1.gene)
r = random.random()
if r < crossoverRate:
# Select a sub-block and swap them between two parents
crossPoint = random.randint(0, gridSize - 1)
tmp = grid1[crossPoint]
grid1[crossPoint] = grid2[crossPoint]
grid2[crossPoint] = tmp
child1.gene = grid1
child2.gene = grid2
else:
child1.gene = grid1
child2.gene = grid2
return child1, child2
def localSearch(self, coef, given):
candidateList = []
for _ in range(coef):
candidate = Candidate()
candidate.gene = npCopy(self.gene)
candidate.localSearchMethod(self, given)
candidateList.append(candidate)
return candidateList
def twoPointcrossover(self, parent1, parent2, crossoverRate):
""" Create two new child candidates by crossing over parent genes.
Parent genes are splitted by two point and then concatenate to generate child genes
Parameters:
- parent1 (Candidate): First parent to crossover
- parent2 (Candidate): Second parent to crossover
- crossoverRate (float): Ratio defines if these parents are crossover or not
Return:
Tuple of two child generate from the crossover process
"""
child1 = Candidate()
child2 = Candidate()
# Make a copy of the parent genes.
grid1 = npCopy(parent1.gene)
grid2 = npCopy(parent2.gene)
gridSize = len(parent1.gene)
r = random.random()
if r < crossoverRate:
# Select two crossover point
crossPoint1 = random.randint(1, gridSize - 2)
crossPoint2 = random.randint(crossPoint1 + 1, gridSize - 1)
# Swap all sub-blocks between two crossover points to generate new child
child1.gene = npConcatenate(
(grid1[:crossPoint1], grid2[crossPoint1:crossPoint2],
grid1[crossPoint2:]),
axis=0)
child2.gene = npConcatenate(
(grid2[:crossPoint1], grid1[crossPoint1:crossPoint2],
grid2[crossPoint2:]),
axis=0)
else:
child1.gene = grid1
child2.gene = grid2
return child1, child2
def rowColCrossover(self, parent1, parent2, crossoverRate):
""" Create two new child candidates by crossing over parent genes.
When two child individuals are generated from two parents, scores are obtained
for each of the three rows that constitute the sub-blocks of the parents,
and a child inherits the ones with the highest scores. Then the columns are
compared in the same way and the other child inherits the ones with the highest scores.
Parameters:
- parent1 (Candidate): First parent to crossover
- parent2 (Candidate): Second parent to crossover
- crossoverRate (float): Ratio defines if these parents are crossover or not
Return:
Tuple of two child generate from the crossover process
"""
child1 = Candidate()
child2 = Candidate()
# Make a copy of the parent genes.
grid1 = parent1.gene
grid2 = parent2.gene
r = random.random()
if r < crossoverRate:
rowScore1 = parent1.fitnessMatrix[0]
rowScore2 = parent2.fitnessMatrix[0]
colScore1 = parent1.fitnessMatrix[1]
colScore2 = parent2.fitnessMatrix[1]
for i in range(3):
# For each row of sub-block, the first child will inherit the row
# with the highest fitness score between two parents
if rowScore1[i] > rowScore2[i]:
child1.gene[3 * i:3 * (i + 1)] = npCopy(grid1[3 * i:3 *
(i + 1)])
else:
child1.gene[3 * i:3 * (i + 1)] = npCopy(grid2[3 * i:3 *
(i + 1)])
# For each col of sub-block, the first child will inherit the col
# with the highest fitness score between two parents
if colScore1[i] > colScore2[i]:
for j in range(3):
child2.gene[j * 3 + i] = npCopy(grid1[j * 3 + i])
else:
for j in range(3):
child2.gene[j * 3 + i] = npCopy(grid2[j * 3 + i])
else:
child1.gene = npCopy(grid1)
child2.gene = npCopy(grid2)
return child1, child2
def nextGen(self, given, tracker):
"""
Find the next generation of the population".
Parameters:
- given (array): The given chromosome of the Sudoku problem, helps in mutation process of candidates
- tracker (array): Helper array to help evaluate candidates' fitness
"""
elites = []
numElite = self.elitism
# Extract top candidate from population. These elite candidates will
# go to the next generation without any change
for i in range(numElite):
elite = Candidate()
elite.gene = npCopy(self.candidates[i].gene)
elites.append(elite)
selectCandidates = selection.call(self.candidates,
self.populationSize - numElite)
newPopulation = []
for _ in range(0, self.populationSize - numElite, 2):
# Select 2 parents
parents = [selectCandidates.pop(), selectCandidates.pop()]
# parents = selection.call(self.candidates, 2)
# Crossover them to generate new child for next generation with a crossover rate
child1, child2 = crossover.call(parents[0], parents[1],
self.crossoverRate)
# Add child to the next genration population
newPopulation.append(child1)
newPopulation.append(child2)
self.candidates = newPopulation
# Mutate candidates in the next generation with a mutation rate
list(map(lambda x: x.mutate(self.mutationRate, given),
self.candidates))
self.candidates.extend(elites)
# Evaluate fitness for the next generation
self.evaluate(tracker)
