def selection(self):
pos1 = generateNewValue(0, self.__param['popSize'] - 1)
pos2 = generateNewValue(0, self.__param['popSize'] - 1)
if (self.__population[pos1].fitness < self.__population[pos2].fitness):
return pos1
else:
return pos2
def __init__(self, problParam=None):
self.__problParam = problParam
self.__repres = [
generateNewValue(problParam['min'], problParam['max'])
for _ in range(problParam['noDim'])
]
self.__fitness = 0.0
def __init__(self, problParam=None):
self.__problParam = problParam
self.__fitness = 0.0
self.__repres = [
generateNewValue(1, self.__problParam['noNodes'])
for _ in range(self.__problParam['noNodes'])
]
def mutationWithProbability(self, mutationProbability):
# each gene it may or may not be swap with another one according to a probability
for i in range(1, self.__problParam['noNodes']):
probability = generateNewValue(0, 1)
if probability < mutationProbability:
pos = randint(1, self.__problParam['noNodes'] - 1)
self.__repres[i], self.__repres[pos] = self.__repres[
pos], self.__repres[i]
def desen(ret):
seed(1)
# plot the function to be optimised
noDim = 1
xref = [[generateNewValue(0, ret['noNodes'] - 1) for _ in range(noDim)]
for _ in range(0, 1000)]
xref.sort()
yref = [fcEval(xi) for xi in xref]
plt.ion()
plt.plot(xref, yref, 'b-')
plt.xlabel('x values')
plt.ylabel('y = f(x) values')
plt.show()
# initialise de GA parameters
# gaParam = {'popSize' : 10, 'noGen' : 3, 'pc' : 0.8, 'pm' : 0.1}
# problem parameters
# problParam = {'min' : MIN, 'max' : MAX, 'function' : fcEval, 'noDim' : noDim, 'noBits' : 8}
# store the best/average solution of each iteration (for a final plot used to anlyse the GA's convergence)
allBestFitnesses = []
allAvgFitnesses = []
generations = []
ga = GA(gaParam, problParam)
ga.initialisation()
ga.evaluation()
for g in range(gaParam['noGen']):
#plotting preparation
allPotentialSolutionsX = [c.repres for c in ga.population]
allPotentialSolutionsY = [c.fitness for c in ga.population]
bestSolX = ga.bestChromosome().repres
bestSolY = ga.bestChromosome().fitness
allBestFitnesses.append(bestSolY)
allAvgFitnesses.append(
sum(allPotentialSolutionsY) / len(allPotentialSolutionsY))
generations.append(g)
plotAFunction(xref, yref, allPotentialSolutionsX,
allPotentialSolutionsY, bestSolX, [bestSolY],
'generation: ' + str(g))
#logic alg
ga.oneGeneration()
# ga.oneGenerationElitism()
# ga.oneGenerationSteadyState()
bestChromo = ga.bestChromosome()
print('Best solution in generation ' + str(g) + ' is: x = ' +
str(bestChromo.repres) + ' f(x) = ' + str(bestChromo.fitness))
plt.ioff()
best, = plt.plot(generations, allBestFitnesses, 'ro', label='best')
mean, = plt.plot(generations, allAvgFitnesses, 'bo', label='mean')
plt.legend([best, (best, mean)], ['Best', 'Mean'])
plt.show()
def binomial_crossover(self, c):
newrepres = []
for i in range(len(self.__repres)):
r = generateNewValue(0, len(self.__repres))
j = uniform(0.0, 1.0)
if r == i or j <= self.__problParam['cr']:
newrepres.append(c.__repres[i])
else:
newrepres.append(self.__repres[i])
offspring = Chromosome(c.__problParam)
offspring.repres = newrepres
return offspring
def mutation(self):
pos = randint(0, len(self.__repres) - 1)
self.__repres[pos] = generateNewValue(self.__problParam['min'], self.__problParam['max'])
def mutation(self):
position = randint(0, self.__problParam['retea']['noNodes'] -
1) # iau o gena la intamplare si pun
# o alta valoare acolo
self.__repres[position] = self.__repres[generateNewValue(
0, self.__problParam['retea']['noNodes'] - 1)]
def mutation(self):
pos = randint(0, self.__problParam['retea']['noNodes'] - 1)
self.__repres[pos] = self.__repres[generateNewValue(
0, self.__problParam['retea']['noNodes'] - 1)]
