def selection_Parent(self, pop):
"""
Tournament selection :
select a parent between K=tournament_Size arrays
and choose the best one
"""
tournament = c.Population(self.configuration_Manager,
self.tournament_Size, False)
for i in range(0, self.tournament_Size):
random_array_index = random.randint(0, pop.population_Size() - 1)
array = pop.get_Array(random_array_index)
tournament.save_Array(i, array)
fittest, pos = tournament.get_Fittest()
return fittest
def __init__(self, parametres):
self.nombre_agents_par_population = parametres[
"nombre_agents_par_population"]
self.taille_agent = parametres["taille_agent"]
number_of_agent_to_select = 2
self.string_to_get = self.init_string_to_get(
self.nombre_agents_par_population)
print("string to get: " + self.string_to_get)
self.population = classes.Population(self.nombre_agents_par_population,
self.taille_agent)
print("Population initiale")
print(self.population)
def Init_Pop(cfg_file, configuration_Manager, source_declination,
source_hour_angle, num_subarrays, list_num_pads_subarrays,
num_arrays, list_objective_constraints, list_constraints_weights,
list_subarrays_weights):
"""
Initialize a population with :
- cfg_file : a file containing the pads to consider
- configuration_Manager : a class wich allows to configurate the population
- num_subarrays : a number of subarrays in each array
- list_num_pads_subarrays : a list of the number of pads per subarrays
- num_arrays : a number of arrays for the population
"""
cfg = pd.read_csv(cfg_file,
comment='#',
names=['E', 'N', 'U', 'diam', 'pad'],
sep='\s+')
cm = configuration_Manager
xx, yy, zz = cfg[['E', 'N', 'U']].values.transpose()
diam = cfg[['diam']].values.transpose()
name = cfg[['pad']].values.transpose()
s = 0
for i in range(0, num_subarrays):
s += list_num_pads_subarrays[i]
cm.clear()
for i in range(0, len(xx)):
pad = c.Pad(xx[i], yy[i], zz[i], diam[0][i], name[0][i])
cm.add_Pad(pad)
cm.set_Source_Declination(source_declination)
cm.set_Source_Hour_Angle(source_hour_angle)
cm.set_Number_Subarrays(num_subarrays)
cm.set_Number_Pads_Per_Subarray(list_num_pads_subarrays)
cm.set_Objective_Constraints(list_objective_constraints)
cm.set_Constraints_Weights(list_constraints_weights)
cm.set_Subarrays_Weights(list_subarrays_weights)
pop = c.Population(cm, num_arrays, True)
return pop
def evolve_Population(self, pop):
"""
Make a new population from the actual population doing :
- elitism : choice of arrays to keep
- selection_parent : choice of parents to cross in order to get knew arrays
- crossover : generate new arrays
- mutation : introduce mutation in the new population
"""
new_pop = c.Population(self.configuration_Manager,
pop.population_Size(), False)
self.elitism(pop, new_pop)
for i in range(self.elitism_Num, new_pop.population_Size()):
parent_1 = self.selection_Parent(pop)
parent_2 = self.selection_Parent(pop)
child = self.crossover(parent_1, parent_2)
new_pop.save_Array(i, child)
for i in range(self.elitism_Num, new_pop.population_Size()):
self.mutation(new_pop.get_Array(i))
return new_pop
def reinit(self):
self.population = classes.Population(self.nombre_agents_par_population,
self.taille_agent)