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Reverted back to old style of attributes.

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This commit is contained in:
danielwilczak101
2020-11-03 11:47:56 -05:00
parent cae4314581
commit 5b6dc8348a
4 changed files with 45 additions and 153 deletions
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63
src/EasyGA.py
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@ -29,66 +29,9 @@ class GA(attributes):
https://github.com/danielwilczak101/EasyGA/wiki
"""
def __init__(self,
chromosome_length = None,
population_size = None,
chromosome_impl = None,
gene_impl = None,
population = None,
target_fitness_type = None,
update_fitness = None,
parent_ratio = None,
selection_probability = None,
tournament_size_ratio = None,
current_generation = None,
current_fitness = None,
generation_goal = None,
fitness_goal = None,
chromosome_mutation_rate = None,
gene_mutation_rate = None,
initialization_impl = None,
fitness_function_impl = None,
make_population = None,
make_chromosome = None,
make_gene = None,
parent_selection_impl = None,
crossover_individual_impl = None,
crossover_population_impl = None,
survivor_selection_impl = None,
mutation_individual_impl = None,
mutation_population_impl = None,
termination_impl = None
):
super(GA, self).__init__(
chromosome_length,
population_size,
chromosome_impl,
gene_impl,
population,
target_fitness_type,
update_fitness,
parent_ratio,
selection_probability,
tournament_size_ratio,
current_generation,
current_fitness,
generation_goal,
fitness_goal,
chromosome_mutation_rate,
gene_mutation_rate,
initialization_impl,
fitness_function_impl,
make_population,
make_chromosome,
make_gene,
parent_selection_impl,
crossover_individual_impl,
crossover_population_impl,
survivor_selection_impl,
mutation_individual_impl,
mutation_population_impl,
termination_impl
)
# Inhert all the ga attributes from the attributes class.
def __init__(self):
super(GA, self).__init__()
def evolve_generation(self, number_of_generations = 1, consider_termination = True):

97
src/attributes.py
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@ -19,85 +19,56 @@ from mutation import Mutation_Methods
from crossover import Crossover_Methods
class attributes:
"""Default GA attributes can be found here"""
"""Default GA attributes can be found here. If any attributes have not
been set then they will fall back onto the default attribute. All
attributes have been catigorized to explain sections in the ga process."""
def __init__(self,
chromosome_length,
population_size,
chromosome_impl,
gene_impl,
population,
target_fitness_type,
update_fitness,
parent_ratio,
selection_probability,
tournament_size_ratio,
current_generation,
current_fitness,
generation_goal,
fitness_goal,
chromosome_mutation_rate,
gene_mutation_rate,
initialization_impl,
fitness_function_impl,
make_population,
make_chromosome,
make_gene,
parent_selection_impl,
crossover_individual_impl,
crossover_population_impl,
survivor_selection_impl,
mutation_individual_impl,
mutation_population_impl,
termination_impl
):
"""Default GA attributes can be found here. If any attributes have not
been set then they will fall back onto the default attribute. All
attributes have been catigorized to explain sections in the ga process."""
def __init__(self):
# Initilization variables
self.chromosome_length = 10 if chromosome_length is None else chromosome_length
self.population_size = 10 if population_size is None else population_size
self.chromosome_impl = chromosome_impl
self.gene_impl = gene_impl
self.population = population
self.target_fitness_type = 'max' if target_fitness_type is None else target_fitness_type
self.update_fitness = True if update_fitness is None else update_fitness
self.chromosome_length = 10
self.population_size = 10
self.chromosome_impl = None
self.gene_impl = lambda: random.randint(1, 10)
self.population = None
self.target_fitness_type = 'max'
self.update_fitness = True
# Selection variables
self.parent_ratio = 0.10 if parent_ratio is None else parent_ratio
self.selection_probability = 0.75 if selection_probability is None else selection_probability
self.tournament_size_ratio = 0.10 if tournament_size_ratio is None else tournament_size_ratio
self.parent_ratio = 0.1
self.selection_probability = 0.75
self.tournament_size_ratio = 0.1
# Termination variables
self.current_generation = 0 if current_generation is None else current_generation
self.current_fitness = 0 if current_fitness is None else current_fitness
self.generation_goal = 15 if generation_goal is None else generation_goal
self.fitness_goal = fitness_goal
self.current_generation = 0
self.current_fitness = 0
self.generation_goal = 15
self.fitness_goal = None
# Mutation variables
self.chromosome_mutation_rate = 0.15 if chromosome_mutation_rate is None else chromosome_mutation_rate
self.gene_mutation_rate = 0.03 if gene_mutation_rate is None else gene_mutation_rate
self.gene_mutation_rate = 0.03
self.chromosome_mutation_rate = 0.15
# Default EasyGA implimentation structure
self.initialization_impl = Initialization_Methods.random_initialization if initialization_impl is None else initialization_impl
self.fitness_function_impl = Fitness_Examples.is_it_5 if fitness_function_impl is None else fitness_function_impl
self.make_population = create_population if create_population is None else create_population
self.make_chromosome = create_chromosome if create_chromosome is None else create_chromosome
self.make_gene = create_gene if create_gene is None else create_gene
self.initialization_impl = Initialization_Methods.random_initialization
self.fitness_function_impl = Fitness_Examples.is_it_5
self.make_population = create_population
self.make_chromosome = create_chromosome
self.make_gene = create_gene
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
self.parent_selection_impl = Parent_Selection.Rank.tournament if parent_selection_impl is None else parent_selection_impl
self.crossover_individual_impl = Crossover_Methods.Individual.single_point if crossover_individual_impl is None else crossover_individual_impl
self.crossover_population_impl = Crossover_Methods.Population.sequential_selection if crossover_population_impl is None else crossover_population_impl
self.survivor_selection_impl = Survivor_Selection.fill_in_best if survivor_selection_impl is None else survivor_selection_impl
self.mutation_individual_impl = Mutation_Methods.Individual.single_gene if mutation_individual_impl is None else mutation_individual_impl
self.mutation_population_impl = Mutation_Methods.Population.random_selection if mutation_population_impl is None else mutation_population_impl
self.parent_selection_impl = Parent_Selection.Rank.tournament
self.crossover_individual_impl = Crossover_Methods.Individual.single_point
self.crossover_population_impl = Crossover_Methods.Population.sequential_selection
self.survivor_selection_impl = Survivor_Selection.fill_in_best
self.mutation_individual_impl = Mutation_Methods.Individual.single_gene
self.mutation_population_impl = Mutation_Methods.Population.random_selection
# The type of termination to impliment
self.termination_impl = Termination_Methods.fitness_and_generation_based if termination_impl is None else termination_impl
self.termination_impl = Termination_Methods.fitness_and_generation_based
# Database varibles
self.database_name = "database.db"
# Getter and setters for all required varibles
@property

1
src/database/database.py
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@ -45,6 +45,7 @@ def create_chromosome(conn, chromosome):
conn.commit()
return cur.lastrowid
def main():
database = r"database.db"

37
src/run_testing.py
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@ -2,34 +2,11 @@ import random
import EasyGA
# Create the Genetic algorithm
# Reproduce 30% of the population.
# Mutate 20% of the population.
# Mutate 3% of the genes in each mutated chromosome.
#
# Create 100 chromosomes each with 25 genes
# Create random genes from 0 to 10
#
# Minimize the sum of the genes
# Terminate when a chromosome has all 0's
# or 150 generations pass
#
ga = EasyGA.GA(
parent_ratio = 0.30,
chromosome_mutation_rate = 0.20,
gene_mutation_rate = 0.03,
population_size = 100,
chromosome_length = 25,
gene_impl = lambda: random.randint(0, 10),
fitness_function_impl = lambda chromosome: sum(gene.get_value() for gene in chromosome.get_gene_list()),
target_fitness_type = 'min',
fitness_goal = 0,
generation_goal = 150
)
ga = EasyGA.GA()
# Run 10 generations at a time
while ga.active():
ga.evolve_generation(10)
ga.print_generation()
ga.print_best()
#ga.print_population()
print('-'*75)
# Evolve the genetic algorithm
ga.evolve()
# Print your default genetic algorithm
ga.print_generation()
ga.print_population()