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Changed implementation framework

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Instead of a nested approach, selection/crossover/mutation are all called separately and directly by the GA. selection_impl was also separated into parent_selection_impl and survivor_selection_impl, as both are needed separately.
This commit is contained in:
RyleyGG
2020-10-04 17:59:59 -04:00
parent c18a531034
commit e05aa7f62b
5 changed files with 102 additions and 120 deletions
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17
src/EasyGA.py
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@ -16,17 +16,17 @@ class GA:
"""Initialize the GA."""
# Initilization variables
self.chromosome_length = 10
self.population_size = 100
self.population_size = 150
self.chromosome_impl = None
self.gene_impl = None
self.population = None
# Termination varibles
self.current_generation = 0
self.current_fitness = 0
self.generation_goal = 50
self.generation_goal = 100
self.fitness_goal = 3
# Mutation variables
self.mutation_rate = 0.05
self.mutation_rate = 0.10
# Rerun already computed fitness
self.update_fitness = True
@ -34,8 +34,9 @@ class GA:
# Defualt EastGA implimentation structure
self.initialization_impl = Initialization_Types().random_initialization
self.fitness_function_impl = Fitness_Examples().is_it_5
self.mutation_impl = Mutation_Types().random_mutation
self.selection_impl = Selection_Types().Tournament().with_replacement
self.mutation_impl = Mutation_Types().per_gene_mutation
self.parent_selection_impl = Selection_Types().Parent_Selection().Tournament().with_replacement
self.survivor_selection_impl = Selection_Types().Survivor_Selection().repeated_crossover
self.crossover_impl = Crossover_Types().single_point_crossover
self.termination_impl = Termination_Types().generation_based
@ -70,7 +71,11 @@ class GA:
self.initialize_population()
self.set_all_fitness(self.population.chromosomes)
next_population = self.selection_impl(self)
self.parent_selection_impl(self)
next_population = self.crossover_impl(self)
next_population = self.survivor_selection_impl(self, next_population)
next_population.set_all_chromosomes(self.mutation_impl(self, next_population.get_all_chromosomes()))
self.population = next_population
self.set_all_fitness(self.population.chromosomes)