def random_repair(self, i, m):
'''transforms an infeasible solution into a feasible solution by random repair'''
while (self.chromosome[i] != str(m)): #find knapsack with exceeded capacity
i = choice(range(1, len(self.chromosome), 2))
if self.chromosome[i - 1] == '1':
c = Chromosome(self.chromosome[0:i - 1] + '00' + self.chromosome[i + 1: len(self.chromosome)])
self.chromosome = c
self.knapsack_items_map = ItemList(ItemParser(RcParser().get_rc_items()).items).get_all_on_items(c, len(self.knapsacks))
return
def exe(self):
for i in range(0, len(self.chromosome), 2):
if choice(range(1, self.probabilty + 1)) == 1:
if self.chromosome[i] == '1':
return Chromosome(self.chromosome[:i] + '00' +
self.chromosome[i + 2:])
elif self.chromosome[i] == '0':
return Chromosome(self.chromosome[:i] + '1' +
str(randint(1,
RcParser().get_m())) +
self.chromosome[i + 2:])
return self.chromosome
def do_selection(self):
'''performs roulette selection'''
fsum = 0
pop_fsum = []
for i in range(len(self.population)):
rc = RcParser()
fitness_function = FitnessFunction(
self.population[i],
ItemList(ItemParser(rc.get_rc_items()).items).get_all_on_items(
self.population[i].solution,
RcParser().get_m()))
fitness = fitness_function.sum_all_fitness()
fsum = fsum + fitness
pop_fsum.append(fitness)
bound = uniform(0, fsum)
curr_fsum = 0
for i in range(len(self.population)):
curr_fsum = curr_fsum + pop_fsum[i]
if curr_fsum >= bound:
return self.population[i]
if __name__ == '__main__':
from parser import RcParser, ItemParser, KnapsackParser
from chromosome import Chromosome
from factory import ChromosomeFactory, PopulationFactory
from crossover import OnePoint
from item import ItemList
from knapsack import KnapsackList
from fitness import FitnessFunction
from selection import RouletteSelection
from population import Population
from random import choice
from mutation import Mutation
from time import sleep
rc = RcParser()
items = ItemList(ItemParser(rc.get_rc_items()).items)
knapsacks = KnapsackList(KnapsackParser(rc.get_rc_knapsacks()).knapsacks)
n = rc.get_n()
m = rc.get_m()
print(
'please note that the number of iterations must be greater than or equal to the number of items'
)
print('number of knapsacks = {0}'.format(m))
print('number of items = {0}'.format(n))
p = 10
print('population size = ' + str(p))
population = PopulationFactory(p, n, m).gen()
iterations = 12
print('iterations = {0}'.format(iterations))
sleep(3)
def gen_item_list():
return ItemList(ItemParser(RcParser().get_rc_items()).items)
if __name__ == '__main__':
from parser import RcParser, ItemParser, KnapsackParser
from chromosome import Chromosome
from factory import ChromosomeFactory, PopulationFactory
from crossover import OnePoint
from item import ItemList
from knapsack import KnapsackList
from fitness import FitnessFunction
from selection import RouletteSelection
from population import Population
from random import choice
from mutation import Mutation
from time import sleep
rc = RcParser()
items = ItemList(ItemParser(rc.get_rc_items()).items)
knapsacks = KnapsackList(KnapsackParser(rc.get_rc_knapsacks()).knapsacks)
n = rc.get_n()
m = rc.get_m()
print('please note that the number of iterations must be greater than or equal to the number of items')
print('number of knapsacks = {0}'.format(m))
print('number of items = {0}'.format(n))
p = 10
print('population size = ' + str(p))
population = PopulationFactory(p, n, m).gen()
iterations = 12
print('iterations = {0}'.format(iterations))
sleep(3)
fittest_chromosome = None
fittest_chromosomes = []
def sum_all_fitness(self):
'''returns total fitness of this chromosome'''
fsum = 0
for knapsack in range(1, RcParser().get_m() + 1):
fsum = fsum + self.sum_single_fitness(knapsack)
return fsum
def get_knapsack_capacity(self, m):
'''returns capacity of knapsack m'''
return RcParser().get_rc_knapsacks()[m]