eckity.termination_checkers.termination_checker 

 1from abc import abstractmethod
 2
 3
 4class TerminationChecker:
 5    """
 6    Abstract TerminationChecker class.
 7
 8    This class is responsible of checking if the evolutionary algorithm should perform early termination.
 9    This class can be expanded depending on the defined termination condition.
10    For example - threshold from target fitness, small change in fitness over a number of generations etc.
11    """
12    @abstractmethod
13    def should_terminate(self, population, best_individual, gen_number):
14        """
15        Determines if the algorithm should perform early termination.
16
17        Parameters
18        ----------
19        population: Population
20            The population of the experiment.
21
22        best_individual: Individual
23            The best individual in the current generation of the algorithm.
24
25        gen_number: int
26            Current generation number.
27
28        Returns
29        -------
30        bool
31            True if the algorithm should terminate early, False otherwise.
32        """
33        pass
class TerminationChecker: 
 5class TerminationChecker:
 6    """
 7    Abstract TerminationChecker class.
 8
 9    This class is responsible of checking if the evolutionary algorithm should perform early termination.
10    This class can be expanded depending on the defined termination condition.
11    For example - threshold from target fitness, small change in fitness over a number of generations etc.
12    """
13    @abstractmethod
14    def should_terminate(self, population, best_individual, gen_number):
15        """
16        Determines if the algorithm should perform early termination.
17
18        Parameters
19        ----------
20        population: Population
21            The population of the experiment.
22
23        best_individual: Individual
24            The best individual in the current generation of the algorithm.
25
26        gen_number: int
27            Current generation number.
28
29        Returns
30        -------
31        bool
32            True if the algorithm should terminate early, False otherwise.
33        """
34        pass

Abstract TerminationChecker class.

This class is responsible of checking if the evolutionary algorithm should perform early termination. This class can be expanded depending on the defined termination condition. For example - threshold from target fitness, small change in fitness over a number of generations etc.

@abstractmethod
def should_terminate(self, population, best_individual, gen_number): 
13    @abstractmethod
14    def should_terminate(self, population, best_individual, gen_number):
15        """
16        Determines if the algorithm should perform early termination.
17
18        Parameters
19        ----------
20        population: Population
21            The population of the experiment.
22
23        best_individual: Individual
24            The best individual in the current generation of the algorithm.
25
26        gen_number: int
27            Current generation number.
28
29        Returns
30        -------
31        bool
32            True if the algorithm should terminate early, False otherwise.
33        """
34        pass

Determines if the algorithm should perform early termination.

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