def __init__(self, genome, seed=None, interactiveMode=True): """ Initializator of GSimpleGA """ if seed: random.seed(seed) if type(interactiveMode) != bool:# BooleanType: Util.raiseException("Interactive Mode option must be True or False", TypeError) if not isinstance(genome, GenomeBase): Util.raiseException("The genome must be a GenomeBase subclass", TypeError) self.internalPop = GPopulation(genome) self.nGenerations = Consts.CDefGAGenerations self.pMutation = Consts.CDefGAMutationRate self.pCrossover = Consts.CDefGACrossoverRate self.nElitismReplacement = Consts.CDefGAElitismReplacement self.setPopulationSize(Consts.CDefGAPopulationSize) self.minimax = Consts.minimaxType["maximize"] self.elitism = True # Adapters self.dbAdapter = None self.migrationAdapter = None self.time_init = None self.max_time = None self.interactiveMode = interactiveMode self.interactiveGen = -1 self.GPMode = False self.selector = FunctionSlot("Selector") self.stepCallback = FunctionSlot("Generation Step Callback") self.terminationCriteria = FunctionSlot("Termination Criteria") self.selector.set(Consts.CDefGASelector) self.allSlots = (self.selector, self.stepCallback, self.terminationCriteria) self.internalParams = {} self.currentGeneration = 0 # GP Testing for classes in Consts.CDefGPGenomes: if isinstance(self.internalPop.oneSelfGenome, classes): self.setGPMode(True) break logging.debug("A GA Engine was created, nGenerations=%d", self.nGenerations)
def step(self): """ Just do one step in evolution, one generation """ newPop = GPopulation(self.internalPop) logging.debug("Population was cloned.") size_iterate = len(self.internalPop) # Odd population size if size_iterate % 2 != 0: size_iterate -= 1 crossover_empty = self.select(popID=self.currentGeneration).crossover.isEmpty() for i in range(0, size_iterate, 2): genomeMom = self.select(popID=self.currentGeneration) genomeDad = self.select(popID=self.currentGeneration) if not crossover_empty and self.pCrossover >= 1.0: for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2): (sister, brother) = it else: if not crossover_empty and Util.randomFlipCoin(self.pCrossover): for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=2): (sister, brother) = it else: sister = genomeMom.clone() brother = genomeDad.clone() sister.mutate(pmut=self.pMutation, ga_engine=self) brother.mutate(pmut=self.pMutation, ga_engine=self) newPop.internalPop.append(sister) newPop.internalPop.append(brother) if len(self.internalPop) % 2 != 0: genomeMom = self.select(popID=self.currentGeneration) genomeDad = self.select(popID=self.currentGeneration) if Util.randomFlipCoin(self.pCrossover): for it in genomeMom.crossover.applyFunctions(mom=genomeMom, dad=genomeDad, count=1): (sister, brother) = it else: sister = random.choice([genomeMom, genomeDad]) sister = sister.clone() sister.mutate(pmut=self.pMutation, ga_engine=self) newPop.internalPop.append(sister) logging.debug("Evaluating the new created population.") newPop.evaluate() if self.elitism: logging.debug("Doing elitism.") if self.getMinimax() == Consts.minimaxType["maximize"]: for i in range(self.nElitismReplacement): #re-evaluate before being sure this is the best self.internalPop.bestRaw(i).evaluate() if self.internalPop.bestRaw(i).score > newPop.bestRaw(i).score: newPop[len(newPop) - 1 - i] = self.internalPop.bestRaw(i) elif self.getMinimax() == Consts.minimaxType["minimize"]: for i in range(self.nElitismReplacement): #re-evaluate before being sure this is the best self.internalPop.bestRaw(i).evaluate() if self.internalPop.bestRaw(i).score < newPop.bestRaw(i).score: newPop[len(newPop) - 1 - i] = self.internalPop.bestRaw(i) self.internalPop = newPop self.internalPop.sort() logging.debug("The generation %d was finished.", self.currentGeneration) self.currentGeneration += 1 if self.max_time: total_time = time() - self.time_init if total_time > self.max_time: return True return self.currentGeneration == self.nGenerations