155 lines
5.4 KiB
Python
155 lines
5.4 KiB
Python
# 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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# Default Attributes for the GA
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from attributes import Attributes
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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 GA(Attributes):
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"""GA is the main class in EasyGA. Everything is run through the ga
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class. The GA class inherites all the default ga attributes from the
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attributes class.
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An extensive wiki going over all major functions can be found at
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https://github.com/danielwilczak101/EasyGA/wiki
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"""
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def __init__(self, attributes = None):
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super(GA, self).__init__({} if attributes is None else attributes)
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def evolve_generation(self, number_of_generations = 1, consider_termination = True):
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"""Evolves the ga the specified number of generations."""
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while(number_of_generations > 0 # Evolve the specified number of generations
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and (not consider_termination # and if consider_termination flag is set
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or self.active())): # then also check if termination conditions reached
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# If its the first generation
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if self.current_generation == 0:
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# Create the database and tables
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# self.database = database.database()
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# self.database.create_data_table(self)
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# Create the initial population
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self.initialize_population()
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self.set_all_fitness()
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self.population.sort_by_best_fitness(self)
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# Otherwise evolve the population
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else:
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self.parent_selection_impl(self)
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self.crossover_population_impl(self)
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self.survivor_selection_impl(self)
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self.population.update()
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self.mutation_population_impl(self)
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self.set_all_fitness()
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self.population.sort_by_best_fitness(self)
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number_of_generations -= 1
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self.current_generation += 1
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def evolve(self):
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"""Runs the ga until the termination point has been satisfied."""
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while(self.active()):
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self.evolve_generation()
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def active(self):
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"""Returns if the ga should terminate based on the termination implimented."""
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return self.termination_impl(self)
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def initialize_population(self):
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"""Initialize the population using
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the initialization implimentation
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that is currently set.
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"""
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self.population = self.initialization_impl(self)
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def set_all_fitness(self):
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"""Will get and set the fitness of each chromosome in the population.
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If update_fitness is set then all fitness values are updated.
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Otherwise only fitness values set to None (i.e. uninitialized
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fitness values) are updated.
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"""
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# Check each chromosome
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for chromosome in self.population.get_chromosome_list():
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# Update fitness if needed or asked by the user
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if(chromosome.get_fitness() is None or self.update_fitness):
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chromosome.set_fitness(self.fitness_function_impl(chromosome))
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def sort_by_best_fitness(self, chromosome_set):
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"""Sorts the array by fitness.
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1st element has highest fitness.
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2nd element has second highest fitness.
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etc.
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"""
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return sorted(chromosome_set, # list to be sorted
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key = lambda chromosome: chromosome.get_fitness(), # by fitness
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reverse = (self.target_fitness_type == 'max')) # from highest to lowest fitness
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def get_chromosome_fitness(self, index):
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"""Returns the fitness value of the chromosome
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at the specified index after conversion based
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on the target fitness type.
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"""
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return self.convert_fitness(
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self.population.get_chromosome(index).get_fitness()
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)
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def convert_fitness(self, fitness_value):
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"""Returns the fitness value if the type of problem
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is a maximization problem. Otherwise the fitness is
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inverted using max - value + min.
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"""
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if self.target_fitness_type == 'max': return fitness_value
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max_fitness = self.population.get_chromosome(-1).get_fitness()
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min_fitness = self.population.get_chromosome(0).get_fitness()
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return max_fitness - fitness_value + min_fitness
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def print_generation(self):
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"""Prints the current generation"""
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print(f"Current Generation \t: {self.current_generation}")
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def print_population(self):
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"""Prints the entire population"""
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self.population.print_all()
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def print_best(self):
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"""Prints the best chromosome and its fitness"""
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print(f"Best Chromosome \t: {self.population.get_chromosome(0)}")
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print(f"Best Fitness \t: {self.population.get_chromosome(0).get_fitness()}")
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