diff --git a/search.py b/search.py
index 68b77a5a8..d136c0135 100644
--- a/search.py
+++ b/search.py
@@ -702,20 +702,11 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
return genetic_algorithm(states[:n], problem.value, ngen, pmut)
-def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1): # noqa
+def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1):
"""[Figure 4.8]"""
for i in range(ngen):
- new_population = []
- random_selection = selection_chances(fitness_fn, population)
- for j in range(len(population)):
- x = random_selection()
- y = random_selection()
- child = reproduce(x, y)
- if random.uniform(0, 1) < pmut:
- child = mutate(child, gene_pool)
- new_population.append(child)
-
- population = new_population
+ population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut)
+ for i in range(len(population))]
if f_thres:
fittest_individual = argmax(population, key=fitness_fn)
@@ -739,18 +730,22 @@ def init_population(pop_number, gene_pool, state_length):
return population
-def selection_chances(fitness_fn, population):
+def select(r, population, fitness_fn):
fitnesses = map(fitness_fn, population)
- return weighted_sampler(population, fitnesses)
+ sampler = weighted_sampler(population, fitnesses)
+ return [sampler() for i in range(r)]
-def reproduce(x, y):
+def recombine(x, y):
n = len(x)
- c = random.randrange(1, n)
+ c = random.randrange(0, n)
return x[:c] + y[c:]
-def mutate(x, gene_pool):
+def mutate(x, gene_pool, pmut):
+ if random.uniform(0, 1) >= pmut:
+ return x
+
n = len(x)
g = len(gene_pool)
c = random.randrange(0, n)
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