示例#1
0
 def test_best_indivividual(self):
     protogene_a = ProtoGene(IntGene, 'a')
     protogenome = ProtoGenome([protogene_a])
     pop = Population(protogenome, optimization_mode='min')
     pop.initialize()    
     for i in range(pop.size):
         pop.individuals[i].score = i
     
     best_ind = pop.best_individual()
     assert pop.individuals[0].score == best_ind.score == 0
     assert pop.individuals[0].get_hash() == best_ind.get_hash()
示例#2
0
 def test_sort(self):
     protogene_a = ProtoGene(IntGene, 'a')
     protogenome = ProtoGenome([protogene_a])
     pop = Population(protogenome, optimization_mode='min')
     pop.initialize()    
     for i in range(pop.size):
         pop.individuals[i].score = i
     pop.sort()
     assert pop.individuals[0].score == 0
     
     pop_2 = Population(protogenome, optimization_mode='max')
     pop_2.initialize(individuals=pop.individuals)
     pop_2.sort()
     assert pop_2.individuals[-1].score == 0
     assert pop_2.individuals[-1].score == pop.individuals[0].score
示例#3
0
 def test_fit_individuals(self):
     protogene_a = ProtoGene(IntGene, 'a', value=1)
     protogenome = ProtoGenome([protogene_a])
     pop = Population(protogenome, optimization_mode='min')
     pop.initialize()    
     def fitness_evaluator(g):
         return g.get_value('a')
     pop.fit_individuals(fitness_evaluator)
     for individual in pop.individuals:
         assert individual.score == 1
     
     dict_cache = DictCache()
     pop.fit_individuals(fitness_evaluator, cache=dict_cache)
     for individual in pop.individuals:
         assert individual.score == 1
         
     self.total_score = 0
     def eval_callback(hash, individual):
         self.total_score += individual.score
         
     pop.fit_individuals(fitness_evaluator, cache=dict_cache, eval_callback=eval_callback)
     assert self.total_score == pop.size
示例#4
0
 def test_evolve(self):
     protogene_a = ProtoGene(IntGene, 'a', value=1)
     protogenome = ProtoGenome([protogene_a])
     pop = Population(protogenome, optimization_mode='min')
     pop.initialize()
     
     def fitness_evaluator(g):
         return g.get_value('a')
     pop.fit_individuals(fitness_evaluator)    
     new_population = pop.evolve()
     
     
     
示例#5
0
class MultiGeneticAlgorithm(object):
    """
    Genetic algorithm class
    
    :param protogenome: instance of :class:`genepi.core.protogenome.Protogenome`
    :param fitness_evaluator: the fitness function
    :param termination_criteria: one or more termination criteria for evolution
    :param termination_criteria_options: options for each termination criteria, each option
     set is passed as a dictionary. Options must be passed in the same order as termination 
     criteria

    
    :param population_size: size of the population
    
    
    """    
    def __init__(self, 
                    protogenome,
                    fitness_evaluator,
                    optimization_mode='min',
                    termination_criteria=stopcriteria.convergence_stop,
                    termination_criteria_options={},
                    population_size=POPULATION_DEFAULT_SIZE,
                    num_populations=4,
                    isolated_cycles=3,
                    step_callback=None,        
                    selection_method=None,
                    elitism=True,
                    num_parents=2,
                    crossover_method=None,
                    crossover_probability=0.1,
                    crossover_wrapper_method = None,
                    mutation_wrapper_method=None,
                    storage_instance=None,
                    cache_instance=None,
                    **options):
                 
        """
        """
        
        self.protogenome = protogenome
        self.fitness_evaluator = fitness_evaluator
        self.optimization_mode = optimization_mode
        self.termination_criteria = termination_criteria 
        self.termination_criteria_options = termination_criteria_options 
        self.population_size = population_size
        self.num_populations = num_populations
        self.isolated_cycles = isolated_cycles
        self.selection_method = selection_method
        self.elitism = elitism
        self.num_parents = num_parents
        self.step_callback = step_callback

        #default crossover and selections are handled by population
        self.crossover_method = crossover_method
        self.crossover_probability = crossover_probability
        
        #mutation wrapper
        self.mutation_wrapper_method = mutation_wrapper_method
        #crossover wrapper
        self.crossover_wrapper_method = crossover_wrapper_method
        
        #cache configuration
        if cache_instance is None:
            self.cache = DictCache()
        else:
            self.cache = cache_instance
        
        #storage configuration
        if storage_instance:
            self.storage = storage_instance
        else:
            self.storage = None
        
        self.options = options
        
        #termination criteria and options check
        if type(self.termination_criteria) == type(list):
            if type(self.termination_criteria_options) != type(list):
                raise ValueError("You must pass options for each termination criteria")
            if len(self.termination_criteria_options) != len(self.termination_criteria):
                raise ValueError("You must pass options for each termination criteria")
        
        self.populations= [];
        self.population = Population(self.protogenome,
                    size=self.population_size, 
                    optimization_mode=self.optimization_mode,
                    selection_method=self.selection_method,
                    crossover_method=self.crossover_method,
                    crossover_probability=self.crossover_probability,
                    elitism=self.elitism,
                    num_parents=self.num_parents,
                    mutation_wrapper_method=self.mutation_wrapper_method,
                    crossover_wrapper_method=self.crossover_wrapper_method)
        
        self.population_stats = {}
        self.current_stats = {}
        
        for i in range(self.num_populations):
            #instantiating population
            pop = Population(self.protogenome,
                    size=self.population_size, 
                    optimization_mode=self.optimization_mode,
                    selection_method=self.selection_method,
                    crossover_method=self.crossover_method,
                    crossover_probability=self.crossover_probability,
                    elitism=self.elitism,
                    num_parents=self.num_parents,
                    mutation_wrapper_method=self.mutation_wrapper_method,
                    crossover_wrapper_method=self.crossover_wrapper_method)
            
            self.populations.append(pop)
            self.population_stats[i] = {}
            self.current_stats[i] = None

        self.generation = 0
        self.main_stats= {}
        self.current_main_stats = None
        
    
    def initialize(self):
        """
        Initializes population, cache and storage
        """
        for i in range(self.num_populations): 
            self.populations[i].initialize()
        self.cache.initialize()
        if self.storage:
            self.storage.initialize()
        
        
    def should_terminate(self):
        """
        Called after each evolution cycle
        """
        if type(self.termination_criteria) == type(list()):
            for i,criterium in enumerate(self.termination_criteria):
                if criterium(self, **self.termination_criteria_options[i]):
                    return True
        else:
            if self.termination_criteria(self, **self.termination_criteria_options):
                return True
                
        return False
        
    def store_individual(self, hash, individual):
        """
        Store an individual in the storage backend.
        """
        if self.storage:    
            self.storage.write_individual(hash, self.generation, individual )

        
    def evaluate_population(self, **options):
        """
        Evaluate current population, the following operations are performed:    
        
        * run fitness_evaluator (compute score on each individual)
        * calculate statistics
        * scale population individuals (compute scaled_score on each individual)
        """
        for i in range(self.num_populations): 
            self.populations[i].fit_individuals(self.fitness_evaluator, self.cache, eval_callback=self.store_individual)
            stats = self.stat_population(pop_id=i, **options)
            self.populations[i].scale_individuals(stats)
        
        
    def stat_population(self, pop_id=None,**options):
        """
        Compute statistics for current population: min max and average scores, idle cycles.
        If a storage is set, stats are written to storage backend.
        After computation, stats are stored in population_stats property, that contains stats
        for all generations. 
        the current_generation property is set to stats.
        
        """
        
        if pop_id is not None:
            
            if pop_id not in self.population_stats:
                self.population_stats[pop_id] = {}
            scores = [individual.score for individual in self.populations[pop_id].individuals]
        else:
            scores = [individual.score for individual in self.population.individuals]
            
        avg_score = sum(scores) / len(scores)
        min_score = min(scores)
        max_score = max(scores)
        
        stats = { 'avg_score' : avg_score, 'min_score' : min_score, 'max_score' : max_score,
            'generation' : self.generation }
        
         
        #setting stats properties       
        if pop_id is not None:
            self.population_stats[pop_id][self.generation] = stats
            self.current_stats[pop_id] = stats
            
            if options.get('debug', None):
                print "pop_id:", pop_id, stats

        else:
            self.main_stats[self.generation] = stats
            self.main_current_stats = stats
            
        
            #idle cycles calculation: it counts consequent generations without improvements
            if self.optimization_mode == 'max':
                top_key = 'max_score'
            else:
                top_key = 'min_score'
    
            if self.generation == 0:
                stats['idle_cycles'] = 0
            else:
                if self.main_stats[self.generation][top_key] == self.main_stats[self.generation-1][top_key]:
                    stats['idle_cycles'] = self.main_stats[self.generation-1]['idle_cycles'] +  1
                else:
                    stats['idle_cycles'] = 0
    
            if options.get('debug', None):
                print stats
    
            if self.storage:
                self.storage.write_population_stats(self.generation, stats)
                
        return stats



    def should_merge_populations(self):
        """
        """
        if self.populations[0].generation_number % self.isolated_cycles == 0:
            return True
        return False
        
        
    def merge_populations(self, **options):
        """
        """
        all_individuals = []
        
        for i in range(self.num_populations): 
            all_individuals += self.populations[i].individuals
        
        all_individuals.sort(self.population.cmp_individual)
        new_individuals = all_individuals[:self.population_size]
        new_population = self.population.copy(individuals=new_individuals)
        self.population = new_population
        stats = self.stat_population( **options)
        self.population.scale_individuals(stats)
        self.generation += 1
        
        self.mix_populations(all_individuals, new_individuals)
        

    def mix_populations(self, all_individuals, champions):
        """
        """
        for i in range(self.num_populations):
            packet = []
            """
            for d in range(10):
                packet.append(random.choice(champions))
            """
            for x in range(self.population_size):
                item = (random.choice(all_individuals))
                packet.append(item)
                all_individuals.remove(item)
    
            self.populations[i].initialize(individuals=packet)
            self.populations[i].sort()
        

    def best_individual(self):
        """
        Returns best individual in population (relies on Population method)
        """
        return self.population.best_individual()
        
        
    def evolve_population(self, **options):
        """
        creates a new population with population.evolve, sets the new population as 
        the current and increment generation.
        After this the population in the previous generation is lost
        """
        for i in range(self.num_populations): 
            new_population = self.populations[i].evolve(**options)
            self.populations[i] = new_population
            
         
    
    def evolve(self, **options):
        """
        Performs the evolution cycle.
        This is the main method that should be normally called.
        Evolution goes on until a termination criterium becomes True.
        At the end the best individual is returned.

        """
        if not self.termination_criteria:
            raise TypeError("You Must set one or more termination criteria")
        
        self.initialize()
        self.evaluate_population(**options)
        
        while 1:
            if self.should_merge_populations():
                self.merge_populations(**options)    
            if self.should_terminate():
                break
            self.evolve_population(global_stats=self.population_stats, last_stats=self.current_stats, ga_engine=self)
            self.evaluate_population(**options)     
            

        #self.merge_populations(**options)                            
        return self.best_individual()
示例#6
0
    def __init__(self, 
                    protogenome,
                    fitness_evaluator,
                    optimization_mode='min',
                    termination_criteria=stopcriteria.convergence_stop,
                    termination_criteria_options={},
                    population_size=POPULATION_DEFAULT_SIZE,
                    num_populations=4,
                    isolated_cycles=3,
                    step_callback=None,        
                    selection_method=None,
                    elitism=True,
                    num_parents=2,
                    crossover_method=None,
                    crossover_probability=0.1,
                    crossover_wrapper_method = None,
                    mutation_wrapper_method=None,
                    storage_instance=None,
                    cache_instance=None,
                    **options):
                 
        """
        """
        
        self.protogenome = protogenome
        self.fitness_evaluator = fitness_evaluator
        self.optimization_mode = optimization_mode
        self.termination_criteria = termination_criteria 
        self.termination_criteria_options = termination_criteria_options 
        self.population_size = population_size
        self.num_populations = num_populations
        self.isolated_cycles = isolated_cycles
        self.selection_method = selection_method
        self.elitism = elitism
        self.num_parents = num_parents
        self.step_callback = step_callback

        #default crossover and selections are handled by population
        self.crossover_method = crossover_method
        self.crossover_probability = crossover_probability
        
        #mutation wrapper
        self.mutation_wrapper_method = mutation_wrapper_method
        #crossover wrapper
        self.crossover_wrapper_method = crossover_wrapper_method
        
        #cache configuration
        if cache_instance is None:
            self.cache = DictCache()
        else:
            self.cache = cache_instance
        
        #storage configuration
        if storage_instance:
            self.storage = storage_instance
        else:
            self.storage = None
        
        self.options = options
        
        #termination criteria and options check
        if type(self.termination_criteria) == type(list):
            if type(self.termination_criteria_options) != type(list):
                raise ValueError("You must pass options for each termination criteria")
            if len(self.termination_criteria_options) != len(self.termination_criteria):
                raise ValueError("You must pass options for each termination criteria")
        
        self.populations= [];
        self.population = Population(self.protogenome,
                    size=self.population_size, 
                    optimization_mode=self.optimization_mode,
                    selection_method=self.selection_method,
                    crossover_method=self.crossover_method,
                    crossover_probability=self.crossover_probability,
                    elitism=self.elitism,
                    num_parents=self.num_parents,
                    mutation_wrapper_method=self.mutation_wrapper_method,
                    crossover_wrapper_method=self.crossover_wrapper_method)
        
        self.population_stats = {}
        self.current_stats = {}
        
        for i in range(self.num_populations):
            #instantiating population
            pop = Population(self.protogenome,
                    size=self.population_size, 
                    optimization_mode=self.optimization_mode,
                    selection_method=self.selection_method,
                    crossover_method=self.crossover_method,
                    crossover_probability=self.crossover_probability,
                    elitism=self.elitism,
                    num_parents=self.num_parents,
                    mutation_wrapper_method=self.mutation_wrapper_method,
                    crossover_wrapper_method=self.crossover_wrapper_method)
            
            self.populations.append(pop)
            self.population_stats[i] = {}
            self.current_stats[i] = None

        self.generation = 0
        self.main_stats= {}
        self.current_main_stats = None
示例#7
0
 def test_mutate(self):
     pop = Population(self.protogenome)
     pop.initialize() 
     pop.mutate()
示例#8
0
 def test_copy(self):
     pop = Population(self.protogenome)
     pop.initialize() 
     pop2 = pop.copy()   
示例#9
0
 def test_initialize(self):
     pop = Population(self.protogenome)
     pop.initialize()    
     assert len(pop.individuals) == pop.size