Update app.py

app.py

@@ -29,12 +29,13 @@ class Net(keras.Model):
 
 # Define a genetic algorithm class
 class GeneticAlgorithm:
-    def __init__(self, population_size):
+    def __init__(self, population_size, task_id):
         self.population_size = population_size
+        self.task_id = task_id
         self.population = [Net() for _ in range(population_size)]
 
-    def selection(self, task_id):
-        X_train, X_test, y_train, y_test = generate_dataset(task_id)
+    def selection(self):
+        X_train, X_test, y_train, y_test = generate_dataset(self.task_id)
         fitness = []
         for net in self.population:
             net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
@@ -82,17 +83,17 @@ num_generations = st.sidebar.slider("Number of generations", 1, 100, 10)
 
 # Run the evolution
 if st.button("Run evolution"):
-    ga = GeneticAlgorithm(population_size)
+    gas = [GeneticAlgorithm(population_size, task_id) for task_id in range(num_tasks)]
     for generation in range(num_generations):
-        for task_id in range(num_tasks):
-            ga.selection(task_id)
+        for ga in gas:
+            ga.selection()
             ga.crossover()
             ga.mutation()
         st.write(f"Generation {generation+1} complete")
 
     # Evaluate the final population
     final_accuracy = []
-    for task_id in range(num_tasks):
+    for task_id, ga in enumerate(gas):
         X_train, X_test, y_train, y_test = generate_dataset(task_id)
         accuracy = []
         for net in ga.population:
@@ -101,4 +102,30 @@ if st.button("Run evolution"):
             loss, acc = net.evaluate(X_test, y_test, verbose=0)
             accuracy.append(acc)
         final_accuracy.append(np.mean(accuracy))
-    st.write(f"Final accuracy: {np.mean(final_accuracy)}")
+        st.write(f"Final accuracy: {np.mean(final_accuracy)}")
+
+    # Trade populations between tasks
+    for i in range(num_tasks):
+        for j in range(i+1, num_tasks):
+            ga1 = gas[i]
+            ga2 = gas[j]
+            population1 = ga1.population
+            population2 = ga2.population
+            num_trade = int(0.1 * population_size)
+            trade1 = random.sample(population1, num_trade)
+            trade2 = random.sample(population2, num_trade)
+            ga1.population = population1 + trade2
+            ga2.population = population2 + trade1
+
+    # Evaluate the final population after trading
+    final_accuracy_after_trade = []
+    for task_id, ga in enumerate(gas):
+        X_train, X_test, y_train, y_test = generate_dataset(task_id)
+        accuracy = []
+        for net in ga.population:
+            net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            net.fit(X_train, y_train, epochs=10, verbose=0)
+            loss, acc = net.evaluate(X_test, y_test, verbose=0)
+            accuracy.append(acc)
+        final_accuracy_after_trade.append(np.mean(accuracy))
+    st.write(f"Final accuracy after trading: {np.mean(final_accuracy_after_trade)}")