def evolve(genome, initial_graph=None, **kwargs):
'''
takes a genome and options that define the genetic algorithm
apply it to the genome
returns infos about the best individual
'''
#depending on the evaluation_method, we will have different goals
goal = emo.get_goal(genome.getParam("evaluation_method"))
multiprocessing = kwargs.get("multiprocessing", False)
#genetic_algorithm = kwargs.get("genetic_algorithm","default_algorithm")
algo = gsga.GSimpleGA(genome)
algo.setMultiProcessing(multiprocessing)
#the selector is used to choose which ones will be parents of the next generation
algo.selector.set(py.Selectors.GRouletteWheel)
#elitism will keep the top individuals with their score in the next generation :
#it can help us to keep track of some of the best graph generations
#algo.setElitism(True)
#algo.setElitismReplacement(10)
algo.setMinimax(py.Consts.minimaxType[goal])
#now we do evolve with algorithm and every freq stats, we compute statistics
freq_stats = int(kwargs.get("freq_stats", "1"))
filename_stats = kwargs.get("stats_path")
stats_adapter = st.StatisticsInTxt(filename=filename_stats,
frequency=freq_stats)
algo.setDBAdapter(stats_adapter)
number_of_generations = int(kwargs.get("nb_generations", "100"))
algo.setGenerations(number_of_generations)
algo.evolve()
def evolve(genome,rule_name,**kwargs):
'''
takes a genome and options that define the genetic algorithm
apply it to the genome
returns infos about the best individual
'''
filename_stats = '../../results/temp/stats_quality_{}.txt'.format(rule_name)
goal = emo.get_goal(genome.getParam("evaluation_method"))
multiprocessing = kwargs.get("multiprocessing",False)
algo = py.GSimpleGA.GSimpleGA(genome)
algo.setMultiProcessing(multiprocessing)
algo.selector.set(py.Selectors.GRouletteWheel)
algo.setMinimax(py.Consts.minimaxType[goal])
stats_adapter = st.StatisticsInTxt(rule_name,frequency = freq_stats)
#stats_adapter = st.StatisticsQualityInTxt(rule_name,filename=filename_stats,frequency = freq_stats)
algo.setDBAdapter(stats_adapter)
number_of_generations = int(kwargs.get("nb_generations","100"))
algo.setGenerations(number_of_generations)
algo.evolve()
return algo.bestIndividual()
def evolve(genome,initial_graph = None,**kwargs):
'''
takes a genome and options that define the genetic algorithm
apply it to the genome
returns infos about the best individual
'''
#depending on the evaluation_method, we will have different goals
goal = emo.get_goal(genome.getParam("evaluation_method"))
multiprocessing = kwargs.get("multiprocessing",False)
#genetic_algorithm = kwargs.get("genetic_algorithm","default_algorithm")
algo = gsga.GSimpleGA(genome)
algo.setMultiProcessing(multiprocessing)
#the selector is used to choose which ones will be parents of the next generation
algo.selector.set(py.Selectors.GRouletteWheel)
#elitism will keep the top individuals with their score in the next generation :
#it can help us to keep track of some of the best graph generations
#algo.setElitism(True)
#algo.setElitismReplacement(10)
algo.setMinimax(py.Consts.minimaxType[goal])
#now we do evolve with algorithm and every freq stats, we compute statistics
freq_stats = int(kwargs.get("freq_stats","1"))
filename_stats = kwargs.get("stats_path")
stats_adapter = st.StatisticsInTxt(filename=filename_stats,frequency = freq_stats)
algo.setDBAdapter(stats_adapter)
number_of_generations = int(kwargs.get("nb_generations","100"))
algo.setGenerations(number_of_generations)
algo.evolve()
def evolve(genome, rule_name, **kwargs):
'''
takes a genome and options that define the genetic algorithm
apply it to the genome
returns infos about the best individual
'''
filename_stats = '../../results/temp/stats_quality_{}.txt'.format(
rule_name)
goal = emo.get_goal(genome.getParam("evaluation_method"))
multiprocessing = kwargs.get("multiprocessing", False)
algo = py.GSimpleGA.GSimpleGA(genome)
algo.setMultiProcessing(multiprocessing)
algo.selector.set(py.Selectors.GRouletteWheel)
algo.setMinimax(py.Consts.minimaxType[goal])
stats_adapter = st.StatisticsInTxt(rule_name, frequency=freq_stats)
#stats_adapter = st.StatisticsQualityInTxt(rule_name,filename=filename_stats,frequency = freq_stats)
algo.setDBAdapter(stats_adapter)
number_of_generations = int(kwargs.get("nb_generations", "100"))
algo.setGenerations(number_of_generations)
algo.evolve()
return algo.bestIndividual()
