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Problem Statement:
Code Type: Implement a Genetic Algorithm to solve the N-Queens problem.
Solution:
The N-Queens problem is a classic puzzle that requires placing N chess queens on an N×N chessboard in such a way that no two queens threaten each other. A Genetic Algorithm (GA) offers an elegant solution to this problem by mimicking the process of natural selection to evolve solutions over successive generations.
import random
def generate_individual(n):
return [random.randint(0, n-1) for _ in range(n)]
def fitness(board):
n = len(board)
conflicts = 0
for i in range(n):
for j in range(i+1, n):
if board[i] == board[j] or abs(board[i] - board[j]) == j - i:
conflicts += 1
return conflicts
def crossover(parent1, parent2):
n = len(parent1)
crossover_point = random.randint(1, n-1)
child = parent1[:crossover_point] + parent2[crossover_point:]
return child
def mutate(individual, mutation_rate):
for i in range(len(individual)):
if random.random() < mutation_rate:
individual[i] = random.randint(0, len(individual)-1)
return individual
def genetic_algorithm(population_size, mutation_rate, max_generations, n):
population = [generate_individual(n) for _ in range(population_size)]
for generation in range(max_generations):
population = sorted(population, key=lambda x: fitness(x))
if fitness(population[0]) == 0:
return population[0]
new_population = [population[0]]
while len(new_population) < population_size:
parent1 = random.choice(population)
parent2 = random.choice(population)
child = crossover(parent1, parent2)
child = mutate(child, mutation_rate)
new_population.append(child)
population = new_population
return None
n = 8 # Change this value for different board sizes
solution = genetic_algorithm(population_size=100, mutation_rate=0.01, max_generations=1000, n=n)
print("Solution for", n, "Queens problem:", solution)
Problem Statement:
Code Type: Develop a Convolutional Neural Network (CNN) for image classification using TensorFlow.
Solution:
Convolutional Neural Networks (CNNs) are powerful tools for image classification tasks due to their ability to automatically learn spatial hierarchies of features from the input images. Let's implement a basic CNN using TensorFlow to classify images from the CIFAR-10 dataset.
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
# Load CIFAR-10 dataset
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0
# Define the CNN architecture
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10)
])
# Compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Train the model
history = model.fit(train_images, train_labels, epochs=10,
validation_data=(test_images, test_labels))
# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('Test accuracy:', test_acc)
In conclusion, mastering Artificial Intelligence assignments requires a blend of theoretical understanding and practical implementation. By leveraging advanced techniques such as Genetic Algorithms and Convolutional Neural Networks, you can tackle complex problems with confidence. Remember, at ProgrammingHomeworkHelp.com, we're here to guide you every step of the way. So, why wait? Dive into the world of AI excellence today!
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