def make_children(self, parent_one, parent_two, num_children):#{{{ children = [] for child in range(num_children):
successful = False
while not successful:
child_members = []
member_pool = parent_one.teamMembers + parent_two.teamMembers
while(len(child_members) < len(parent_one.teamMembers)):
if(random.random() < mutation_rate):
next_member_index = random.randrange(len(member_pool))
child_members.append(member_pool[next_member_index])
member_pool.remove(member_pool[next_member_index])
else:
mutation_member_pool = hirepool.allmembers - child_members
child_members.append(
mutation_member_pool[random.randrange(len(mutation_member_pool))])
new_team = team(child_members)
# if size of memberpool > size of parent_one members
# AND matches from new_team to population.teamMembers or new_team.teamMembers
# if size of memberpool > size of parent_one members AND (
#if population.teamMembers matches new_team.teamMembers anywhere OR
#if children.teamMembers matches new_team.teamMembers anywhere)
if len(member_pool) > len(parent_one.teamMembers) \
and (set(population.teamMembers) & set(new_team.teamMembers) \
or set(children.teamMembers) & set(new_team.teamMembers)):
child -= 1
else:
children.append(new_team)
successful = True
def geneticAlgorithm():
# pareto_front = []
# base_mutation_rate = .05
global mut_rate
mutation_rate = .05
global population
population = []
team_size = 12
max_population_size = 200
tournament_size = 10
if team_size < len(hirepool.allmembers):
for i in max_population_size:
current_member_pool = hirepool.allmembers
new_team_members = []
for j in team_size:
new_member = current_member_pool[random.randrange(len(current_member_pool))]
new_team_members.append(new_member)
current_member_pool.remove(new_member)
population.append(team(new_team_members))
else:
raise Exception("Team size too big!")
emergency_exit = 1000
last_change_in_avg = 0
avg_social = 0
avg_tech = 0
generation = 0
generation_to_log = 1
while generation + 1 < emergency_exit and last_change_in_avg < 10:
set_total_fit()
if len(population) > max_population_size:
population = sorted(population, key=lambda x: x.total_fitness)GetRange(0, maxPopulationSize)
def set_total_fit():
return
def set_indv_fit():
return
def set_dom_ranks():
return
def make_children(self, parent_one, parent_two, num_children):#{{{ children = [] for child in range(num_children):
successful = False
while not successful:
child_members = []
member_pool = parent_one.teamMembers + parent_two.teamMembers
while(len(child_members) < len(parent_one.teamMembers)):
if(random.random() < mutation_rate):
next_member_index = random.randrange(len(member_pool))
child_members.append(member_pool[next_member_index])
member_pool.remove(member_pool[next_member_index])
else:
mutation_member_pool = hirepool.allmembers - child_members
child_members.append(
mutation_member_pool[random.randrange(len(mutation_member_pool))])
new_team = team(child_members)
# if size of memberpool > size of parent_one members
# AND matches from new_team to population.teamMembers or new_team.teamMembers
# if size of memberpool > size of parent_one members AND (
#if population.teamMembers matches new_team.teamMembers anywhere OR
#if children.teamMembers matches new_team.teamMembers anywhere)
if len(member_pool) > len(parent_one.teamMembers) \
and (set(population.teamMembers) & set(new_team.teamMembers) \
or set(children.teamMembers) & set(new_team.teamMembers)):
child -= 1
else:
children.append(new_team)
successful = True
return children#}}}
