class Algorithm:
def __init__(self):
self.__problem = Problem()
self.__dimPopulation = 100
self.__dimIndividual = 2
self.__min = self.__problem.getMinVal()
self.__max = self.__problem.getMaxVal()
self.__population = Population(self.__dimPopulation,
self.__dimIndividual,
self.__problem.getInitialSet())
self.__x = 0
def getProblem(self):
return self.__problem
def getData(self):
return self.__data
def getPopulation(self):
return self.__population
def readParameters(self, file_name):
return 0
def iteration(self, pM):
i1 = randint(0, len(self.__population.getListOfIndivids()) - 1)
i2 = randint(0, len(self.__population.getListOfIndivids()) - 1)
listOfIndivids = self.__population.getListOfIndivids()
# child
c = listOfIndivids[i1].crossover(listOfIndivids[i2])
c.mutate(pM)
f1 = listOfIndivids[i1].fitness(self.__problem)
f2 = listOfIndivids[i2].fitness(self.__problem)
fc = c.fitness(self.__problem)
if f1 > fc:
listOfIndivids[i1].setSets(c.getSets())
if f2 > fc:
listOfIndivids[i2].setSets(c.getSets())
return self.__population
def run(self, noIterations=10000):
pM = self.__dimPopulation / noIterations
for i in range(noIterations):
p = self.iteration(pM)
self.statistics()
def statistics(self):
#print the best individual
graded = [(x.fitness(self.__problem), x)
for x in self.__population.getListOfIndivids()]
for x in graded:
print("\nfitness: " + str(x[0]) + "\n" + str(x[1]) + "\n\n")
graded.sort(key=lambda x: x[0], reverse=True)
result = graded[0]
fitnessOptim = result[0]
individualOptim = result[1]
print('\nThe best solution is:\n' + str(result[1]) +
' \nIt has its fitness = ' + str(result[0]) + "\n")
