Use utils.argmax_random_tie for best-individual selection in tsp.py (#1297)

np.argmax does not accept a key argument (TypeError) and np.argmax_random_tie does not exist (argmax_random_tie lives in utils). Use utils.argmax_random_tie in both the genetic algorithm and hill climbing loops of the TSP GUI. Fixes #1256.

Author: mohsinm-dev

gui/tsp.py

@@ -260,7 +260,7 @@ def fitness_fn(state):
         while True:
             population = [mutate(recombine(*select(2, population, fitness_fn)), self.mutation_rate.get())
                           for _ in range(len(population))]
-            current_best = np.argmax(population, key=fitness_fn)
+            current_best = utils.argmax_random_tie(population, key=fitness_fn)
             if fitness_fn(current_best) > fitness_fn(all_time_best):
                 all_time_best = current_best
                 self.cost.set("Cost = " + str('%0.3f' % (-1 * problem.value(all_time_best))))
@@ -294,7 +294,7 @@ def find_neighbors(state, number_of_neighbors=100):
         current = Node(problem.initial)
         while True:
             neighbors = find_neighbors(current.state, self.no_of_neighbors.get())
-            neighbor = np.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state))
+            neighbor = utils.argmax_random_tie(neighbors, key=lambda node: problem.value(node.state))
             map_canvas.delete('poly')
             points = []
             for city in current.state: