from multilayer_perceptron import MLPSurrogate from ea import * from benchmarks import zdt2, load_theo import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) # Load theoritical values theo = load_theo('./ZDT/ZDT2.pf') # ZDT2 PM_FUN = zdt2 DIMENSION = 30 POP_SIZE = 64 MAX_GENERATION = 25 MAX_EPISODE = 30 MUTATION_RATE = 0.08 MUTATION_U = 0. MUTATION_ST = 0.2 REF=[1., 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,
from premade import GOMORS from surrogates import heuristic_optimizer import matplotlib.pyplot as plt from matplotlib import rc from sklearn.gaussian_process.kernels import RBF, WhiteKernel as W,\ ConstantKernel as C # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) theo = load_theo('pareto_Kursawe.txt').as_matrix() # Kursawe PM_FUN = kursawe MINIMIZE = True DIMENSION = 3 N_OBJS = 2 POP_SIZE = 64 MAX_GENERATION = 40 MAX_EPISODE = 60 STOPPING_RULE = 'max_eval' MUTATION_RATE = 0.1 MUTATION_U = 0. MUTATION_ST = 0.3 REF = [-14., 0.1]
from _utils import gaussian_mutator, nsga_crossover, random_crossover from premade import GOMORS import matplotlib.pyplot as plt from matplotlib import rc from pySOT.kernels import CubicKernel from pySOT.tails import LinearTail # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) # Load theoritical values theo = load_theo('./ZDT/ZDT1.pf').as_matrix() # ZDT1 PM_FUN = zdt1 MINIMIZE = True DIMENSION = 30 POP_SIZE = 64 N_OBJS = 2 MAX_GENERATION = 30 MAX_EPISODE = 120 STOPPING_RULE = 'max_eval' MUTATION_RATE = 0.1 MUTATION_U = 0. MUTATION_ST = 0.2 REF = [1., 1.]
from multilayer_perceptron import MLPSurrogate # from multioutput import MultiOutputRegressor from _utils import * from ea import Population from benchmarks import load_theo, kursawe import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) theo = load_theo('pareto_Kursawe.txt') # Kursawe PM_FUN = kursawe DIMENSION = 3 POP_SIZE = 64 MAX_GENERATION = 25 MAX_EPISODE = 30 MUTATION_RATE = 0.07 MUTATION_U = 0. MUTATION_ST = 0.3 REF = [-14., 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,
import numpy as np import pandas as pd from multilayer_perceptron import MLPSurrogate from neuro_surrogate import * from benchmarks import zdt1, load_theo import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) # Load theoritical values theo = load_theo('./ZDT/ZDT1.pf') # ZDT1 PM_FUN = zdt1 DIMENSION = 30 POP_SIZE = 64 MAX_GENERATION = 25 MAX_EPISODE = 30 MUTATION_RATE = 0.08 MUTATION_U = 0. MUTATION_ST = 0.2 REF = [1., 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,
sys.path.append('..') from multilayer_perceptron import MLPSurrogate # from multioutput import MultiOutputRegressor from ea import * from benchmarks import load_theo, zdt6 import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) theo = load_theo('./ZDT/ZDT6.pf') # ZDT6 PM_FUN = zdt6 DIMENSION = 10 POP_SIZE = 64 MAX_GENERATION = 25 MAX_EPISODE = 30 MUTATION_RATE = 0.08 MUTATION_U = 0. MUTATION_ST = 0.2 REF = [1., 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,
import numpy as np import pandas as pd from multilayer_perceptron import MLPSurrogate from neuro_surrogate import * from benchmarks import zdt3, load_theo import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) # Load theoritical values theo = load_theo('./ZDT/ZDT3.pf') # ZDT3 PM_FUN = zdt3 DIMENSION = 30 POP_SIZE = 64 MAX_GENERATION = 25 MAX_EPISODE = 30 MUTATION_RATE = 0.08 MUTATION_U = 0. MUTATION_ST = 0.2 REF = [0.9, 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,
import numpy as np import pandas as pd from multilayer_perceptron import MLPSurrogate from neuro_surrogate import * from benchmarks import zdt4, load_theo import matplotlib.pyplot as plt from matplotlib import rc # Matplotlib rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica']}) # for Palatino and other serif fonts use: # rc('font',**{'family':'serif','serif':['Palatino']}) rc('text', usetex=True) # Load theoritical values theo = load_theo('./ZDT/ZDT4.pf') # ZDT4 PM_FUN = zdt4 DIMENSION = 10 POP_SIZE = 32 MAX_GENERATION = 25 MAX_EPISODE = 128 MUTATION_RATE = 0.1 MUTATION_U = 0. MUTATION_ST = 0.02 REF = [1., 1.] pop = Population(dim=DIMENSION, size=POP_SIZE, fitness_fun=PM_FUN,