Neural Evolution

Evolution.py

# Read Data
data = pd.read_csv('banknote.csv')
# Create Matrix of Independent Variables
X = data.drop(['Y'], axis=1)
# Create Vector of Dependent Variable
y = data['Y']
# Create a Train Test Split for Genetic Optimization
X_train, X_test, y_train, y_test = train_test_split(X, y)
# Create a List of all active GeneticNeuralNetworks
networks = []
pool = []
# Track Generations
generation = 0
# Initial Population
n = 20

# Generate n randomly weighted neural networks
for i in range(0, n):
    networks.append(GeneticNeuralNetwork())

# Cache Max Fitness
max_fitness = 0

# Max Fitness Weights
optimal_weights = []

# Evolution Loop
while max_fitness < .9:
    # Log the current generation
    generation += 1
    print('Generation: ', generation)

    # Forward propagate the neural networks to compute a fitness score
    for nn in networks:
        # Propagate to calculate fitness score
        nn.forward_propagation(X_train, y_train)
        # Add to pool after calculating fitness
        pool.append(nn)

    # Clear for propagation of next children
    networks.clear()

    # Sort based on fitness
    pool = sorted(pool, key=lambda x: x.fitness)
    pool.reverse()

    # Find Max Fitness and Log Associated Weights
    for i in range(0, len(pool)):
        # If there is a new max fitness among the population
        if pool[i].fitness > max_fitness:
            max_fitness = pool[i].fitness
            print('Max Fitness: ', max_fitness)
            # Reset optimal_weights
            optimal_weights = []
            # Iterate through layers, get weights, and append to optimal
            for layer in pool[i].layers:
                optimal_weights.append(layer.get_weights()[0])
            print(optimal_weights)

    # Crossover, top 5 randomly select 2 partners for child
    for i in range(0, 5):
        for j in range(0, 2):
            # Create a child and add to networks
            temp = dynamic_crossover(pool[i], random.choice(pool))
            # Add to networks to calculate fitness score next iteration
            networks.append(temp)

# Create a Genetic Neural Network with optimal initial weights
gnn = GeneticNeuralNetwork(optimal_weights)
gnn.compile_train(10)

# Test the Genetic Neural Network Out of Sample
y_hat = gnn.predict(X_test.values)
print('Test Accuracy: %.2f' % accuracy_score(y_test, y_hat.round()))

Can I know where is the dynamic crossover is imported from.
And also where did the cross over used
Do answer so that it will be useful for the people