Search: Update Genetic Algorithm To New Pseudocode

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)