toolbox = base.Toolbox()
toolbox.register("evalfit", FitnessFunction)
toolbox.register("evalfitsh", FitSharing, fitFunction=toolbox.evalfit, distanceFunction=NormHamming2, sigma=0.2)
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=IND_SIZE)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selStochasticUniversalSampling, fit_attr='fitshare')
# toolbox.register("select", tools.selTournament, tournsize=3, fit_attr='fitshare')
population = toolbox.population(n=NUM_IND)
AllBinSeq = GenBinSeq(IND_SIZE)
AllPossibleFits = [FitnessFunction(i)[0] for i in AllBinSeq]
NumSequences=[NormBinSeqToNum(i) for i in AllBinSeq]
fig, ax = plt.subplots()
ax.plot(NumSequences, AllPossibleFits, '-r')
scatt = ax.scatter([NormBinSeqToNum(i) for i in population],[FitnessFunction(i)[0] for i in population])
def animate(frame, population=population, toolbox=toolbox):
offspring = algorithms.varAnd(population, toolbox, cxpb=0.1, mutpb=0)
for ind in offspring:
ind.fitshare.values = toolbox.evalfitsh(ind, offspring)
population = toolbox.select(offspring, k=len(population))
xdata=np.asarray([NormBinSeqToNum(i) for i in population])
ydata=np.asarray([FitnessFunction(i)[0] for i in population])
scatt.set_array(np.c_(xdata,ydata))
plt.subplot(142)
plt.plot([i for i in range(NUM_GEN)],mpr_val)
plt.title('generation VS MaxPeakRatio')
plt.xlabel('generation')
plt.ylabel('MaxPeakRatio')
plt.grid(True)
plt.subplot(143)
num_gen=[i for i in range(NUM_GEN)]
plt.plot(num_gen, MaxFitness, 'r', label='MaxFitness')
plt.plot(num_gen, AvgFitness, 'g', label='AvgFitness')
plt.plot(num_gen, MinFitness, 'b', label='MinFitness')
plt.title('generation VS fitness')
plt.xlabel('generation')
plt.ylabel('Fitness')
plt.grid(True)
plt.legend()
# niches graphical rapresentation
plt.subplot(144)
AllBinSeq = GenBinSeq(10)
AllPossibleFits = [FitnessFunction(i)[0] for i in AllBinSeq]
PlotBinSeq(AllBinSeq, AllPossibleFits, 'r')
ScatBinFct(population, FitnessFunction)
plt.title('fitness VS individuals')
plt.xlabel('Individuals')
plt.ylabel('Fitness')
plt.grid(True)
plt.show()
#!/usr/bin/python3 import matplotlib.pyplot as plt from FitFunctions import FnctA, FnctB from utilities import GenBinSeq, PlotBinFct, NormBinSeqToNum # individual size: IND_SIZE = 8 PlotBinFct(GenBinSeq(IND_SIZE), FnctA) PlotBinFct(GenBinSeq(IND_SIZE), FnctB, '--r') plt.show()