chavalitros
/
MH-P2
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Implement population selection

This commit is contained in:
coolneng 2021-06-18 19:33:26 +02:00
parent c450d65870
commit ccf3a18a59
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 14 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
src/genetic_algorithm.py
View File

@ -148,10 +148,10 @@ def mutate(population, n, probability=0.001):
return population return population
def tournament_selection(population): def tournament_selection(m, population):
individuals = population.sample(n=2) individuals = [population[randint(m)] for _ in range(2)]
best_index = population["distance"].idxmax() best_index = population.index(max(population, key=lambda x: all(x.fitness)))
return individuals.iloc[best_index] return individuals[best_index]
def generational_replacement(previous_population, current_population): def generational_replacement(previous_population, current_population):
@ -200,12 +200,20 @@ def evaluate_population(population, data, cores=4):
return evaluated_population return evaluated_population
def select_new_population(population, n, m, mode):
if mode == "generational":
parents = [tournament_selection(m, population) for _ in range(n)]
else:
parents = [tournament_selection(m, population) for _ in range(2)]
return parents
def genetic_algorithm(n, m, data, mode, max_iterations=100000): def genetic_algorithm(n, m, data, mode, max_iterations=100000):
population = [generate_individual(n, m, data) for _ in range(n)] population = [generate_individual(n, m, data) for _ in range(n)]
population = evaluate_population(population, data) population = evaluate_population(population, data)
for _ in range(max_iterations): for _ in range(max_iterations):
pass parents = select_new_population(population, n, m, mode)
n, m, data = parse_file("data/GKD-c_11_n500_m50.txt") n, m, data = parse_file("data/GKD-c_11_n500_m50.txt")
genetic_algorithm(n=10, m=5, data=data, mode="") genetic_algorithm(n=10, m=5, data=data, mode="generational", max_iterations=1)