eckity.multi_objective_evolution.crowding_termination_checker 

 1from eckity.termination_checkers.termination_checker import TerminationChecker
 2
 3
 4class CrowdingTerminationChecker(TerminationChecker):
 5	"""
 6	Concrete Termination Checker that checks the distance from best existing fitness value to target fitness value.
 7
 8	Parameters
 9	----------
10	threshold: float, default=0.1
11		what is the maximal value of crowding that should be allowed in he population
12	"""
13
14	def __init__(self, threshold=0.1):
15		super().__init__()
16		self.threshold = threshold
17
18	def should_terminate(self, population=None, best_individual=None, gen_number=None):
19		"""
20		allawys return false so the program wont terminate until it finishes its iteration
21		Parameters
22		----------
23		population: Population
24			The evolutionary experiment population of individuals.
25
26		best_individual: Individual
27			The individual that has the best fitness of the current generation.
28
29		gen_number: int
30			Current generation number.
31
32		Returns
33		-------
34		bool
35			True if the algorithm should terminate early, False otherwise.
36		"""
37		max_crwding_in_pop = self._find_max_crowding(population)
38		return max_crwding_in_pop < self.threshold and max_crwding_in_pop != 0
39
40	def _find_max_crowding(self, population):
41		crowdings = []
42		for sup_pop in population.sub_populations:
43			crowdings.append(
44				max([ind.fitness.crowding for ind in sup_pop.individuals if ind.fitness.crowding != float("inf")]))
45		return max(crowdings)
class CrowdingTerminationChecker(eckity.termination_checkers.termination_checker.TerminationChecker): 
 5class CrowdingTerminationChecker(TerminationChecker):
 6	"""
 7	Concrete Termination Checker that checks the distance from best existing fitness value to target fitness value.
 8
 9	Parameters
10	----------
11	threshold: float, default=0.1
12		what is the maximal value of crowding that should be allowed in he population
13	"""
14
15	def __init__(self, threshold=0.1):
16		super().__init__()
17		self.threshold = threshold
18
19	def should_terminate(self, population=None, best_individual=None, gen_number=None):
20		"""
21		allawys return false so the program wont terminate until it finishes its iteration
22		Parameters
23		----------
24		population: Population
25			The evolutionary experiment population of individuals.
26
27		best_individual: Individual
28			The individual that has the best fitness of the current generation.
29
30		gen_number: int
31			Current generation number.
32
33		Returns
34		-------
35		bool
36			True if the algorithm should terminate early, False otherwise.
37		"""
38		max_crwding_in_pop = self._find_max_crowding(population)
39		return max_crwding_in_pop < self.threshold and max_crwding_in_pop != 0
40
41	def _find_max_crowding(self, population):
42		crowdings = []
43		for sup_pop in population.sub_populations:
44			crowdings.append(
45				max([ind.fitness.crowding for ind in sup_pop.individuals if ind.fitness.crowding != float("inf")]))
46		return max(crowdings)

Concrete Termination Checker that checks the distance from best existing fitness value to target fitness value.

Parameters
  • threshold (float, default=0.1): what is the maximal value of crowding that should be allowed in he population
CrowdingTerminationChecker(threshold=0.1) 
15	def __init__(self, threshold=0.1):
16		super().__init__()
17		self.threshold = threshold
threshold
def should_terminate(self, population=None, best_individual=None, gen_number=None): 
19	def should_terminate(self, population=None, best_individual=None, gen_number=None):
20		"""
21		allawys return false so the program wont terminate until it finishes its iteration
22		Parameters
23		----------
24		population: Population
25			The evolutionary experiment population of individuals.
26
27		best_individual: Individual
28			The individual that has the best fitness of the current generation.
29
30		gen_number: int
31			Current generation number.
32
33		Returns
34		-------
35		bool
36			True if the algorithm should terminate early, False otherwise.
37		"""
38		max_crwding_in_pop = self._find_max_crowding(population)
39		return max_crwding_in_pop < self.threshold and max_crwding_in_pop != 0

allawys return false so the program wont terminate until it finishes its iteration

Parameters
  • population (Population): The evolutionary experiment population of individuals.
  • best_individual (Individual): The individual that has the best fitness of the current generation.
  • gen_number (int): Current generation number.
Returns
  • bool: True if the algorithm should terminate early, False otherwise.