def update(self):
state = self._algorithm.get_state()
# self._state = {
# "iteration" : self._generation,
# "message" : message,
# "content" : content,
# "fittest" : self._fittest
#}
# Parse
iteration = 0
if "iteration" in state: iteration = state["iteration"]
message = ""
if "message" in state: message = state["message"]
content = ""
if "content" in state: content = state["content"]
fittest = None
if "fittest" in state: fittest = state["fittest"]
if message == "Genetic Algorithm":
Terminal.print_box(messages=[message], font_color=FontColor.Green)
elif message == "Configuration":
print(message)
print(content)
elif message == "Fittest Solution":
if fittest:
Terminal.print_box(
messages=[message, f"fitness = {fittest.fitness}"],
font_color=FontColor.Green)
else:
if fittest:
print(
f"Generation {iteration:7d} - fitness: {fittest.fitness}")
def _build_solution(self):
Terminal.print_box(messages=[
f"Build Solution - ENCODING: Can repeat = {self._problem_instance.encoding.can_repeat_elements} - Is Ordered = {self._problem_instance.encoding.is_ordered }"
])
solution = None
for _ in range(0, 8):
try:
solution = self._problem_instance.build_solution()
print(f"solution = {solution.representation}")
except:
self._errors.append("Build Solution Failed!")
if solution == None:
print(
" - Error: Check if build solution is OK or if it was implemented"
)
def run(self):
Terminal.print_box(messages=[
f"Neighborhood Function - ENCODING: Can repeat = {self._problem_instance.encoding.can_repeat_elements} - Is Ordered = {self._problem_instance.encoding.is_ordered }"
])
for _ in range(0, 2):
solution = self._problem_instance.build_solution()
neighbors = self._neighborhood_function(
solution=solution,
problem=self._problem_instance,
neighborhood_size=self._neighborhood_size)
Terminal.print_line(
message=f"\nNeighbors of {solution.representation} are:")
for neighbor in neighbors:
print(f" {neighbor.representation} ")
def _evaluate(self):
Terminal.print_box(messages=["Objective Function"])
feedback = None
if self._require_feedback:
feedback = self._problem_instance.build_solution()
for _ in range(0, 8):
solution = self._problem_instance.build_solution()
self._problem_instance.evaluate_solution(solution=solution,
feedback=feedback)
print(
f" solution = {solution.representation} | feedback = { feedback.representation } | fitness = {solution.fitness}"
)
else:
for _ in range(0, 8):
solution = self._problem_instance.build_solution()
self._problem_instance.evaluate_solution(solution=solution)
print(
f" solution = {solution.representation} | fitness = {solution.fitness}"
)
def _is_admissible(self):
Terminal.print_box(messages=[
f"Is Admissible Solution - {self._problem_instance.constraints}"
])
Terminal.print_line(" Constraints DV ")
print(self._constraint_dv)
solution = None
for _ in range(0, 8):
try:
solution = self._problem_instance.build_solution()
is_admissible = self._problem_instance.is_admissible(solution)
print(
f"solution = {solution.representation} - Is admissible? {is_admissible}"
)
except:
self._errors.append("Is admissible solution Failed!")
if solution == None:
print(
" - Error: Check if build solution is OK or if it was implemented"
)
def run(self):
self._errors = []
Terminal.clear()
Terminal.print_box(messages=["Name: " + self._problem_instance._name],
font_color=FontColor.Yellow)
try:
print(f" Objective = {self._problem_instance.objective}")
except:
print("ERROR: " + self._problem_instance.objective)
self._errors.append()
print(
f" Multi-objective = {self._problem_instance.is_multi_objective}")
print(f" Encoding Rule = {self._problem_instance.encoding_rule }")
self._build_solution()
self._is_admissible()
self._evaluate()
def update(self):
state = self._algorithm.get_state()
# {
# "problem" : self._problem_instance,
# "iteration" : self._iteration,
# "message" : message,
# "solution" : self._solution,
# "neighbor" : self._neighbor
# }
message = ""
if "message" in state: message = state["message"]
# started
if message == LocalSearchMessage.Started:
Terminal.clear()
Terminal.print_box(
messages=[self._algorithm.name, self._algorithm.description],
font_color=FontColor.Green)
Terminal.print_box(
messages=["Problem: " + self._algorithm.problem.name],
font_color=FontColor.Yellow)
# initialized
elif message == LocalSearchMessage.Initialized:
Terminal.print_box(messages=["Initialized"])
Terminal.print_line(message="Initial Solution:")
print(
f" Solution: {self._algorithm.solution.representation} - fitness: {self._algorithm.solution.fitness}"
)
Terminal.print_box(messages=["Iterations"])
elif message == LocalSearchMessage.ReplacementAccepted:
iteration = -1
if "iteration" in state:
iteration = state["iteration"]
Terminal.print_line(
message=
f"Iteration {iteration:10d} | Solution: {self._algorithm.solution.representation } | Fitness: {self._algorithm.solution.fitness}"
)
elif message == LocalSearchMessage.ReplacementRejected:
iteration = -1
if "iteration" in state:
iteration = state["iteration"]
Terminal.print_line(
message=
f"Iteration {iteration:10d} | Solution: {self._algorithm.solution.representation } | Fitness: {self._algorithm.solution.fitness} *** (no change)"
)
elif message == LocalSearchMessage.StoppedPrematurely:
Terminal.print_box(messages=["Stopped Prematurely!"],
font_color=FontColor.Yellow)
elif message == LocalSearchMessage.Stopped:
Terminal.print_box(messages=["Stopped Max Iterations!"])
elif message == LocalSearchMessage.StoppedTargetAchieved:
Terminal.print_box(messages=["Stopped Target Achieved!"],
font_color=FontColor.Green)