151 lines
6.6 KiB
Python
151 lines
6.6 KiB
Python
import random
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import sqlite3
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from copy import deepcopy
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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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# Database class
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from database import database
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from sqlite3 import Error
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class Attributes:
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"""Default GA attributes can be found here. If any attributes have not
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been set then they will fall back onto the default attribute. All
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attributes have been catigorized to explain sections in the ga process."""
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def __init__(self,
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chromosome_length = 10,
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population_size = 10,
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chromosome_impl = None,
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gene_impl = lambda: random.randint(1, 10),
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population = None,
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target_fitness_type = 'max',
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update_fitness = True,
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parent_ratio = 0.10,
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selection_probability = 0.50,
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tournament_size_ratio = 0.10,
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current_generation = 0,
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current_fitness = 0,
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generation_goal = 15,
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fitness_goal = None,
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chromosome_mutation_rate = 0.15,
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gene_mutation_rate = 0.03,
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initialization_impl = Initialization_Methods.random_initialization,
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fitness_function_impl = Fitness_Examples.is_it_5,
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make_population = create_population,
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make_chromosome = create_chromosome,
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make_gene = create_gene,
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parent_selection_impl = Parent_Selection.Rank.tournament,
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crossover_individual_impl = Crossover_Methods.Individual.single_point,
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crossover_population_impl = Crossover_Methods.Population.sequential_selection,
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survivor_selection_impl = Survivor_Selection.fill_in_best,
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mutation_individual_impl = Mutation_Methods.Individual.single_gene,
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mutation_population_impl = Mutation_Methods.Population.random_selection,
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termination_impl = Termination_Methods.fitness_and_generation_based,
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database = None,
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database_name = 'database.db',
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sql_create_data_structure = """CREATE TABLE IF NOT EXISTS data (
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id integer PRIMARY KEY,
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generation integer NOT NULL,
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fitness DOUBLE,
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chromosome text
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); """
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):
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# Initilization variables
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self.chromosome_length = deepcopy(chromosome_length)
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self.population_size = deepcopy(population_size)
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self.chromosome_impl = deepcopy(chromosome_impl)
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self.gene_impl = deepcopy(gene_impl)
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self.population = deepcopy(population)
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self.target_fitness_type = deepcopy(target_fitness_type)
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self.update_fitness = deepcopy(update_fitness)
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# Selection variables
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self.parent_ratio = deepcopy(parent_ratio)
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self.selection_probability = deepcopy(selection_probability)
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self.tournament_size_ratio = deepcopy(tournament_size_ratio)
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# Termination variables
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self.current_generation = deepcopy(current_generation)
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self.current_fitness = deepcopy(current_fitness)
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self.generation_goal = deepcopy(generation_goal)
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self.fitness_goal = deepcopy(fitness_goal)
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# Mutation variables
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self.chromosome_mutation_rate = deepcopy(chromosome_mutation_rate)
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self.gene_mutation_rate = deepcopy(gene_mutation_rate)
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# Default EasyGA implimentation structure
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self.initialization_impl = deepcopy(initialization_impl)
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self.fitness_function_impl = deepcopy(fitness_function_impl)
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self.make_population = deepcopy(make_population)
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self.make_chromosome = deepcopy(make_chromosome)
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self.make_gene = deepcopy(make_gene)
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# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
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self.parent_selection_impl = deepcopy(parent_selection_impl)
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self.crossover_individual_impl = deepcopy(crossover_individual_impl)
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self.crossover_population_impl = deepcopy(crossover_population_impl)
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self.survivor_selection_impl = deepcopy(survivor_selection_impl)
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self.mutation_individual_impl = deepcopy(mutation_individual_impl)
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self.mutation_population_impl = deepcopy(mutation_population_impl)
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# The type of termination to impliment
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self.termination_impl = deepcopy(termination_impl)
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# Database varibles
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self.database = deepcopy(database)
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self.database_name = deepcopy(database_name)
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self.sql_create_data_structure = deepcopy(sql_create_data_structure)
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# Getter and setters for all required varibles
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@property
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def chromosome_length(self):
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"""Getter function for chromosome length"""
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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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"""Setter function with error checking for chromosome length"""
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# If the chromosome length is less then or equal 0 throw error
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if(not isinstance(value_input, int) or value_input <= 0):
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raise ValueError("Chromosome length must be integer 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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"""Getter function for population size"""
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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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"""Setter function with error checking for population size"""
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# If the population size is less then or equal 0 throw error
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if(not isinstance(value_input, int) or value_input <= 0):
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raise ValueError("Population length must be integer greater then 0")
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self._population_size = value_input
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