update

Author: MorvanZhou

tutorial-contents/Genetic Algorithm/Find Path.py

@@ -51,7 +51,7 @@ def select(self, fitness):
 
     def crossover(self, parent, pop):
         if np.random.rand() < self.cross_rate:
-            i_ = np.random.choice(np.arange(self.pop_size), size=1, replace=False)  # select another individual from pop
+            i_ = np.random.randint(0, self.pop_size, size=1)  # select another individual from pop
             cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points
             parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child
         return parent

tutorial-contents/Genetic Algorithm/Genetic Algorithm Basic.py

@@ -33,9 +33,9 @@ def select(pop, fitness):    # nature selection wrt pop's fitness
 
 def crossover(parent, pop):     # mating process (genes crossover)
     if np.random.rand() < CROSS_RATE:
-        i_ = np.random.randint(0, POP_SIZE, 1)                  # select another individual from pop
-        cross_points = np.random.randint(0, 2, DNA_SIZE).astype(np.bool)    # choose crossover points
-        parent[cross_points] = pop[i_, cross_points]                        # mating and produce one child
+        i_ = np.random.randint(0, POP_SIZE, size=1)                             # select another individual from pop
+        cross_points = np.random.randint(0, 2, size=DNA_SIZE).astype(np.bool)   # choose crossover points
+        parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child
     return parent
 
 

tutorial-contents/Genetic Algorithm/Match Phrase.py

@@ -41,7 +41,7 @@ def select(self):
 
     def crossover(self, parent, pop):
         if np.random.rand() < self.cross_rate:
-            i_ = np.random.choice(np.arange(self.pop_size), size=1)                 # select another individual from pop
+            i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop
             cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points
             parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child
         return parent

tutorial-contents/Genetic Algorithm/Microbial Genetic Algorithm.py

@@ -39,14 +39,14 @@ def get_fitness(self, product):
     def crossover(self, loser_winner):      # crossover for loser
         cross_idx = np.empty((self.DNA_size,)).astype(np.bool)
         for i in range(self.DNA_size):
-            cross_idx[i] = True if np.random.uniform(0, 1) < self.cross_rate else False  # crossover index
+            cross_idx[i] = True if np.random.rand() < self.cross_rate else False  # crossover index
         loser_winner[0, cross_idx] = loser_winner[1, cross_idx]  # assign winners genes to loser
         return loser_winner
 
     def mutate(self, loser_winner):         # mutation for loser
         mutation_idx = np.empty((self.DNA_size,)).astype(np.bool)
         for i in range(self.DNA_size):
-            mutation_idx[i] = True if np.random.uniform(0, 1) < self.mutate_rate else False  # mutation index
+            mutation_idx[i] = True if np.random.rand() < self.mutate_rate else False  # mutation index
         # flip values in mutation points
         loser_winner[0, mutation_idx] = ~loser_winner[0, mutation_idx].astype(np.bool)
         return loser_winner

tutorial-contents/Genetic Algorithm/Travel Sales Person.py

@@ -71,7 +71,7 @@ def evolve(self, fitness):
 
 class TravelSalesPerson(object):
     def __init__(self, n_cities):
-        self.city_position = np.random.uniform(0, 1, size=(n_cities, 2))
+        self.city_position = np.random.rand(n_cities, 2)
         plt.ion()
 
     def plotting(self, lx, ly, total_d):