def codificarCromosoma(self, intervalo): self.cromosoma = '' #Recibe el intervalo y usa el extremo superior del mismo para calcular el numero de bits del cromosoma ultimoEnBin = "{0:b}".format(intervalo[-1]) longitudCromosoma = len(ultimoEnBin) for x in range(longitudCromosoma): gen = Gen() self.cromosoma = self.cromosoma + gen.getAlelo()
def onClose(self, event): self.result = {'name': self.nameVar, 'io_intensive': self.ioIntensiveVar, 'range': (int(self.startAddressVar), int(self.endAddressVar)), 'count': int(self.ioCountVar), 'write_percent': float(self.writePercentageVar), 'write_ran_percent': float(self.randomWritePercentageVar), 'read_ran_percent': float(self.randomReadPercentageVar)} print(self.result) myGen = Gen(self.result) myGen.gen()
def breed(): # Select parent from the this generation selected_parents = select() # Add them to new generation new_generation = selected_parents[:] # Shuffle it to keep the variety of population shuffle(selected_parents) # Let them cross over to create 1 child each couple # The 1/4 population remain will be generate randomly children = [] for i in range(0, len(selected_parents), 2): children += selected_parents[i].cross_over(selected_parents[i + 1]) new_generation.append(Gen(life_time.get())) # Let the children mutate mutation(children) # Add children to new generation new_generation += children # Clear out the population walkers.clear() # Create new population from the selected gens for i in range(len(new_generation)): walkers.append( Walker(map_blocks, map_blocks[start_point[0]][start_point[1]], new_generation[i], game_canvas)) # Track the best walker walkers[0].best = True
def __init__(self, screen, point): self.screen = screen self.generation = 1 self.population_size = 50 min_x = -screen[0] // 2 self.jumpers = [ JumpMan(screen=screen, dna=Gen(random.randint(min_x, 0)), point=point) for _ in range(self.population_size) ]
def init_population(): # Randomly create the initial population # Size of POPULATION_SIZE Walker.block_size = block_size.get() Walker.life_time = life_time.get() Walker.goal_point = goal_point population = [] for i in range(population_size.get()): population.append( Walker(map_blocks, map_blocks[start_point[0]][start_point[1]], Gen(life_time.get()), game_canvas)) return population
def __init__(self, width, height): self.delay = 0.0000005 self.screen = [width, height] self.wn = turtle.Screen() self.wn.title("Jump Man Game") self.wn.bgcolor("black") self.wn.setup(width=width, height=height) self.wn.tracer(0) self.roof = Roof(width, height) self.floor = Floor(width, height, self.roof.point) gen = Gen(0, 30, 20) # self.jump_man = JumpMan(screen=self.screen, dna=gen, point=self.floor.point) self.population = Population(screen=self.screen, point=self.floor.point)
def __init__(self): Gen.__init__(self, self.__class__.__name__)
def __init__(self, name): Gen.__init__(self, name) self.attrs['freq'] = 220.0 self.attrs['phase'] = 0.0 self.attrs['sync'] = 2.0
from Gen import Gen #2GB data, 20GB address range myGen = Gen({ 'range': (1,0.5*1024*1024*1024//512), 'count': 2*1024*1024*1024//4096, 'write_percent': 0.5, 'write_ran_percent': 0.67, 'read_ran_percent': 0.67 }) myGen.gen()
# Ralph Pereira # COSC3P71 Assignment 2 # TSP using a GA ################################################################### #import statements from Gen import Gen import numpy as np import csv #create the two maps b1 = Gen("berlin52.tsp", 0.0, 1.0) d1 = Gen("dj38.tsp", 0.0, 1.0) b2 = Gen("berlin52.tsp", 0.1, 1.0) d2 = Gen("dj38.tsp", 0.1, 1.0) b3 = Gen("berlin52.tsp", 0.0, 0.9) d3 = Gen("dj38.tsp", 0.0, 0.9) b4 = Gen("berlin52.tsp", 0.1, 0.9) d4 = Gen("dj38.tsp", 0.1, 0.9) b5 = Gen("berlin52.tsp", 0.05, 0.95) d5 = Gen("dj38.tsp", 0.05, 0.95) #main logic for the test file creatation def CalculatePath(m, genCount, filename): #open the file with the file name provided wf = open(filename, "w") wf.write("Generation No.\tBest Distance\tAverage Distance\n") #create the maps and calculate the fitness fit = m.CalculateFitness(m.GetTO()) bestOrder = m.FindBest(m.travelOrder) travelDistance = m.CalculateTotalDistance(m.GetTO()) #run it for "genCount" amount of Generations
def main(): genA = Gen(None, 'genA') genB = Gen(None, 'genB') genC = Gen(None, 'genC') genD = Gen(None, 'genD') genE = Gen(None, 'genE') genF = Gen(None, 'genF') genA.add_patch_to(genB) genB.add_patch_to(genC) genB.add_patch_to(genD) genD.add_patch_to(genA) genD.add_patch_to(genE) Patch.dfs_patch_search(genA) for patch in Patch.patch_list: print patch genB.add_patch_to(genF) Patch.dfs_patch_search(genA) for patch in Patch.patch_list: print patch