def reset(self):
print "[Genetic Search]\n-> Initializing population of size: %s" % self.__pop_size
self.__pop = []
# vector of still unused parameters
# this is done to avoid to have some of the parameters out of the first population
unused_params = [param for param in self.__config.parameters.values()]
ratio = int(math.ceil(float(len(self.__config)) / float(self.__pop_size)))
while len(self.__pop) < self.__pop_size:
# the gene length for this individual will be chosen randomly between 2 and n
length = random.randint(1, len(self.__config))
element = Configuration()
for gene in range(length):
if gene < ratio and len(unused_params) != 0:
idx = random.randint(0, len(unused_params) - 1)
param = unused_params[idx]
param.rand()
element += param
del unused_params[idx]
continue
# we choose each gene randomly
param = None
while param is None:
idx = random.randint(0, len(self.__config) - 1)
param = self.__config.parameters.values()[idx]
if not param in element.parameters.values():
param.rand()
element += param
else:
param = None
# remove from the list of mandatory params
if param in unused_params:
unused_params.remove(param)
added_params = []
for param in self.__config.parameters.values():
if param.name not in element.parameter_keys():
element += param
added_params.append(param)
if element.check(self.__constraints):
self.__pop.append(element)
for param in added_params:
element -= param
def crossover(self, p1, p2):
offsprings = []
# Crossover
for offspring_idx in range(2):
# decide the size of the offspring
offspring = Configuration()
lb = min(len(p1), len(p2))
ub = max(len(p1), len(p2))
# we choose the size randomly between the 2 parents' size
size = random.randint(lb, ub)
common = set()
for p1_gene in p1.parameters.values():
if p1_gene in p2.parameters.values():
# two parents have the same gene
if random.randint(0, 1):
offspring += p1_gene
else:
offspring += p2.parameters[p1_gene.name]
common.add(p1_gene)
# print ','.join(map(lambda x: x.name, common))
remaining = list((set(p1.parameters.values()) - common) | (set(p2.parameters.values()) - common))
# print ','.join(map(lambda x: x.name, remaining))
while len(offspring) < size:
# select from the remaining genes
idx = random.randint(0, len(remaining) - 1)
if remaining[idx].name not in offspring.parameter_keys():
offspring += remaining[idx]
del remaining[idx]
offsprings.append(offspring)
return tuple(offsprings)