def crossover(self, state1, state2):
cut_point = randint(0, itemSize - 1)
child = state1[:cut_point] + state2[cut_point:]
return child
def mutate(self, state):
mutation = choice('01')
mutation_point = randint(0, itemSize - 1)
mutated = ''.join([
state[i] if i != mutation_point else mutation
for i in range(len(state))
])
return mutated
problem = knapsack(initial_state='0101')
result = genetic(problem, mutation_chance=0.1, viewer=WebViewer())
print(result.state)
print(result.path())
'''
problem = knapsack(initial_state='0101')
result = hill_climbing(problem, viewer=ConsoleViewer())
print(result.state)
print(result.path())
'''
'''
problem = knapsack(initial_state='0101')
result = hill_climbing_random_restarts(problem, viewer=ConsoleViewer())
print(result.state)
print(result.path())
'''
attacks = 0
for queen_col, queen_row in enumerate(state):
for queen2_col, queen2_row in enumerate(state):
if queen_col < queen2_col:
# no son la misma reina, chequeemos ataques
if queen_row == queen2_row:
# están en la misma fila
attacks += 1
else:
# están en la misma diagonal?
cols_diff = abs(queen_col - queen2_col)
rows_diff = abs(queen_row - queen2_row)
if cols_diff == rows_diff:
# están en la misma diagonal
attacks += 1
return -attacks
problem = GeneticQueensProblem(None)
result = genetic(problem,
population_size=150,
mutation_chance=0.1,
iterations_limit=100)
print(result.state)
print("Attacks:", -problem.value(result.state))
def value(self, s): v = 0 for index, item in enumerate(s): if item == 1: v += self.values[index] return v def _weight(self, s): weight = 0 for index, item in enumerate(s): if item == 1: weight += self.weights[index] return weight def _valid(self, s): if self._weight(s) > maxWeight: return False return True if __name__ == "__main__": numObjects = 20 weights = [4, 6, 5, 5, 3, 2, 4, 8, 1, 5, 3, 7, 2, 5, 6, 3, 8, 4, 7, 2] values = [5, 6, 2, 8, 6, 5, 8, 2, 7, 6, 1, 3, 4, 4, 1, 5, 6, 2, 5, 3] maxWeight = 35 problem = KnapsackProblem(numObjects, maxWeight, weights, values) result = genetic(problem, iterations_limit=100, population_size=16, mutation_chance=0.10) print result.path() print 'Weight = ' + str(problem._weight(result.path()[0][1])) print 'Value = ' + str(problem.value(result.path()[0][1]))