88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import random
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# Import all the data structure prebuilt modules
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from structure import Population as create_population
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from structure import Chromosome as create_chromosome
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from structure import Gene as create_gene
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# Structure Methods
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from fitness_function import Fitness_Examples
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from initialization import Initialization_Methods
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from termination_point import Termination_Methods
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# Parent/Survivor Selection Methods
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from parent_selection import Parent_Selection
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from survivor_selection import Survivor_Selection
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# Genetic Operator Methods
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from mutation import Mutation_Methods
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from crossover import Crossover_Methods
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class attributes:
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def __init__(self):
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"""Initialize the GA."""
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# Initilization variables
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self.chromosome_length = 10
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self.population_size = 10
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self.chromosome_impl = None
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self.gene_impl = lambda: random.randint(1, 10)
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self.population = None
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self.target_fitness_type = 'max'
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self.update_fitness = True
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# Selection variables
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self.parent_ratio = 0.1
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self.selection_probability = 0.75
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self.tournament_size_ratio = 0.1
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# Termination variables
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self.current_generation = 0
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self.current_fitness = 0
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self.generation_goal = 15
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self.fitness_goal = None
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# Mutation variables
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self.mutation_rate = 0.10
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# Default EasyGA implimentation structure
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self.initialization_impl = Initialization_Methods.random_initialization
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self.fitness_function_impl = Fitness_Examples.is_it_5
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self.make_population = create_population
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self.make_chromosome = create_chromosome
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self.make_gene = create_gene
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# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
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self.parent_selection_impl = Parent_Selection.Rank.tournament
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self.crossover_individual_impl = Crossover_Methods.Individual.single_point
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self.crossover_population_impl = Crossover_Methods.Population.random_selection
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self.survivor_selection_impl = Survivor_Selection.fill_in_best
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self.mutation_individual_impl = Mutation_Methods.Individual.single_gene
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self.mutation_population_impl = Mutation_Methods.Population.random_selection
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# The type of termination to impliment
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self.termination_impl = Termination_Methods.fitness_and_generation_based
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# Getter and setters for all varibles
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@property
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def chromosome_length(self):
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return self._chromosome_length
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@chromosome_length.setter
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def chromosome_length(self, value_input):
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if(value_input == 0):
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raise ValueError("Chromosome length must be greater then 0")
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self._chromosome_length = value_input
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@property
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def population_size(self):
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return self._population_size
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@population_size.setter
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def population_size(self, value_input):
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if(value_input == 0):
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raise ValueError("Population length must be greater then 0")
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self._population_size = value_input
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