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Fixed attributes to allow setting attributes on initialization

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e.g.
GA({
    'chromosome_length' : 10,
    'population_size'         : 50
})
This commit is contained in:
SimpleArt
2020-11-03 13:19:09 -05:00
parent 5b6dc8348a
commit 621fdef86d
2 changed files with 73 additions and 36 deletions
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100
src/attributes.py
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@ -18,57 +18,95 @@ from survivor_selection import Survivor_Selection
from mutation import Mutation_Methods
from crossover import Crossover_Methods
class attributes:
class Attributes:
"""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):
def __init__(self, attributes):
# Default settings for the GA
default_attributes = {
'chromosome_length' : 10,
'population_size' : 10,
'chromosome_impl' : None,
'gene_impl' : lambda: random.randint(1, 10),
'population' : None,
'target_fitness_type' : 'max',
'update_fitness' : True,
'parent_ratio' : 0.10,
'selection_probability' : 0.50,
'tournament_size_ratio' : 0.10,
'current_generation' : 0,
'current_fitness' : 0,
'generation_goal' : 15,
'fitness_goal' : None,
'chromosome_mutation_rate' : 0.15,
'gene_mutation_rate' : 0.03,
'initialization_impl' : Initialization_Methods.random_initialization,
'fitness_function_impl' : Fitness_Examples.is_it_5,
'make_population' : create_population,
'make_chromosome' : create_chromosome,
'make_gene' : create_gene,
'parent_selection_impl' : Parent_Selection.Rank.tournament,
'crossover_individual_impl' : Crossover_Methods.Individual.single_point,
'crossover_population_impl' : Crossover_Methods.Population.sequential_selection,
'survivor_selection_impl' : Survivor_Selection.fill_in_best,
'mutation_individual_impl' : Mutation_Methods.Individual.single_gene,
'mutation_population_impl' : Mutation_Methods.Population.random_selection,
'termination_impl' : Termination_Methods.fitness_and_generation_based,
'database_name' : 'database.db'
}
# Filling in the default settings
for attribute in default_attributes.keys():
if attribute not in attributes.keys():
attributes[attribute] = default_attributes[attribute]
# Initilization variables
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
self.chromosome_length = attributes['chromosome_length']
self.population_size = attributes['population_size']
self.chromosome_impl = attributes['chromosome_impl']
self.gene_impl = attributes['gene_impl']
self.population = attributes['population']
self.target_fitness_type = attributes['target_fitness_type']
self.update_fitness = attributes['update_fitness']
# Selection variables
self.parent_ratio = 0.1
self.selection_probability = 0.75
self.tournament_size_ratio = 0.1
self.parent_ratio = attributes['parent_ratio']
self.selection_probability = attributes['selection_probability']
self.tournament_size_ratio = attributes['tournament_size_ratio']
# Termination variables
self.current_generation = 0
self.current_fitness = 0
self.generation_goal = 15
self.fitness_goal = None
self.current_generation = attributes['current_generation']
self.current_fitness = attributes['current_fitness']
self.generation_goal = attributes['generation_goal']
self.fitness_goal = attributes['fitness_goal']
# Mutation variables
self.gene_mutation_rate = 0.03
self.chromosome_mutation_rate = 0.15
self.chromosome_mutation_rate = attributes['chromosome_mutation_rate']
self.gene_mutation_rate = attributes['gene_mutation_rate']
# Default EasyGA implimentation structure
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
self.initialization_impl = attributes['initialization_impl']
self.fitness_function_impl = attributes['fitness_function_impl']
self.make_population = attributes['make_population']
self.make_chromosome = attributes['make_chromosome']
self.make_gene = attributes['make_gene']
# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation
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
self.parent_selection_impl = attributes['parent_selection_impl']
self.crossover_individual_impl = attributes['crossover_individual_impl']
self.crossover_population_impl = attributes['crossover_population_impl']
self.survivor_selection_impl = attributes['survivor_selection_impl']
self.mutation_individual_impl = attributes['mutation_individual_impl']
self.mutation_population_impl = attributes['mutation_population_impl']
# The type of termination to impliment
self.termination_impl = Termination_Methods.fitness_and_generation_based
self.termination_impl = attributes['termination_impl']
# Database varibles
self.database_name = "database.db"
self.database_name = attributes['database_name']
# Getter and setters for all required varibles
@property