]
for count, i in enumerate(n):
if i[count] == 1:
i[count] = 0
elif i[count] == 0:
i[count] = 1
n = [Individual(i) for i in n]
return n
# Monkey Patching
Individual.evaluate = evaluate
Individual.get_neighbours = get_neighbours
pop = Population(size=20,
optim="max",
sol_size=len(values),
valid_set=[0, 1],
replacement=True)
pop.evolve(
gens=100,
select=fps,
crossover=single_point_co,
mutate=binary_mutation,
co_p=0.7,
mu_p=0.2,
elitism=False,
)
for count, i in enumerate(n):
i[count], i[count + 1] = i[count + 1], i[count]
n = [Individual(i) for i in n]
return n
# Monkey patching
Individual.evaluate = evaluate
Individual.get_neighbours = get_neighbours
pop = Population(
size=100,
sol_size=len(distance_matrix[0]),
valid_set=[i for i in range(len(distance_matrix[0]))],
replacement=False,
optim="min",
)
pop.evolve(
gens=100,
select= rank,
crossover= pmx_co,
mutate=swap_mutation,
co_p=0.8,
mu_p=0.5,
elitism=True
)
indexes around in pairs.
Returns:
list: a list of individuals
"""
n = [
deepcopy(self.representation)
for i in range(len(self.representation) - 1)
]
for count, i in enumerate(n):
i[count], i[count + 1] = i[count + 1], i[count]
n = [Individual(i) for i in n]
return n
# Monkey patching
Individual.evaluate = evaluate
Individual.get_neighbours = get_neighbours
pop = Population(
size=20,
sol_size=len(distance_matrix[0]),
valid_set=[i for i in range(len(distance_matrix[0]))],
replacement=False,
optim="min",
)
hill_climb(pop, log=1)
"""
return self.representation.count(1)
def get_neighbours(self):
n = [
deepcopy(self.representation) for i in range(len(self.representation))
]
for count, i in enumerate(n):
if i[count] == 1:
i[count] = 0
elif i[count] == 0:
i[count] = 1
n = [Individual(i) for i in n]
return n
Individual.evaluate = evaluate
Individual.get_neighbours = get_neighbours
pop = Population(size=20,
optim="max",
sol_size=4,
valid_set=[0, 1],
replacement=True)
hill_climb(pop)
sim_annealing(pop)
stopped_early = []
for epoch in range(config['epochs']):
# collect data
puzzle = collect_puzzle_data(config)
# compute metadata on board shapes
init_positions = find_init_positions(puzzle.puzzle_flat)
# Monkey Patching
Individual.create_representation = resolve_create_representation(
config, puzzle)
Individual.evaluate = resolve_evaluate_fct(puzzle)
Individual.get_neighbours = None
pop = Population(size=config['pop_size'], optim=config['optim'])
pop.evolve(
gens=config['gens'],
select=globals()[config['selection']],
crossover=resolve_crossover(
config,
init_positions), # define operator in function above
mutate=resolve_mutation(
config,
init_positions), # define operator in function above
co_p=config['co_p'],
mu_p=config['mu_p'],
elitism=config['elitism'],
fitness_sharing=config['fitness_sharing'],
diversity_measure=config['diversity_measure'],
elif i[count] == 0:
i[count] = 1
n = [Individual(i) for i in n]
return n
# Monkey Patching
Individual.evaluate = evaluate
Individual.get_neighbours = get_neighbours
def create_representation(self):
sol_size = len(values)
valid_set = [0, 1]
return [choice(valid_set) for i in range(sol_size)]
Individual.create_representation = create_representation
if __name__ == '__main__':
pop = Population(size=100, optim="max")
pop.evolve(gens=100,
select=fps,
crossover=single_point_co,
mutate=binary_mutation,
co_p=0.7,
mu_p=0.2,
elitism=False)
print('ok')