📅 最后修改于: 2023-12-03 14:49:07.728000 🧑 作者: Mango
人工智能搜索算法是一类运用人工智能技术，通过建立问题的模型，在问题的状态空间中搜索最佳解的算法。这类算法广泛应用于人工智能领域的各个子领域中，如机器学习、数据挖掘等。
深度优先搜索算法是一种盲目搜索算法，是从该问题的一个初始结点开始，一直搜索到图中某一目标结点，或者无法到达任何目标结点时才停止。DFS 通过一系列先进后出的堆栈来实现搜索过程，它的搜索顺序是从当前顶点的邻接顶点中选择一个顶点放入栈中，然后以该顶点作为起点再递归搜索。
def dfs(graph, start, goal):
visited = set()
stack = [start]
while stack:
vertex = stack.pop()
if vertex == goal:
return True
visited.add(vertex)
for neighbor in graph[vertex]:
if neighbor not in visited:
stack.append(neighbor)
return False
广度优先搜索算法是一种盲目搜索算法，是从该问题的一个初始结点开始，按照广度优先遍历图的顺序逐个搜索各个结点，直到到达图中目标结点或者未搜索到为止。BFS 通过一系列先进先出的队列来实现搜索过程，它的搜索顺序是从当前顶点的邻接顶点中选择一个顶点放入队列中，然后取出队列头顶点再递归搜索。
from collections import deque
def bfs(graph, start, goal):
visited = set()
queue = deque([start])
while queue:
vertex = queue.popleft()
if vertex == goal:
return True
visited.add(vertex)
for neighbor in graph[vertex]:
if neighbor not in visited:
queue.append(neighbor)
return False
一致代价搜索算法是一种启发式搜索算法，是从该问题的一个初始结点开始，按照代价从小到大的顺序逐步搜索各个结点，直到到达图中目标结点或者未搜索到为止。UCS 通过优先队列来实现搜索过程，它的搜索顺序是根据优先级依次取出最小代价的结点进行扩展搜索。
from queue import PriorityQueue
def ucs(graph, start, goal):
visited = set()
pq = PriorityQueue()
pq.put((0, [start]))
while pq:
(cost, path) = pq.get()
vertex = path[-1]
if vertex == goal:
return path
visited.add(vertex)
for neighbor in graph[vertex]:
if neighbor not in visited:
new_cost = cost + graph[vertex][neighbor]
new_path = path + [neighbor]
pq.put((new_cost, new_path))
return None
A* 搜索算法是一种启发式搜索算法，是在 UCS 算法的基础上加上启发式函数求出优先级，从而使搜索过程更加智能化。A* 通过优先队列来实现搜索过程，它的搜索顺序是根据优先级依次取出最小代价的结点进行扩展搜索。
def heuristic(node, goal):
return abs(node[0] - goal[0]) + abs(node[1] - goal[1])
def astar(graph, start, goal):
visited = set()
pq = PriorityQueue()
pq.put((0, start))
while pq:
(cost, node) = pq.get()
if node == goal:
return True
visited.add(node)
for neighbor in graph[node]:
if neighbor not in visited:
new_cost = cost + graph[node][neighbor] + heuristic(neighbor, goal)
pq.put((new_cost, neighbor))
return False
爬山算法是一种局部搜索算法，是在当前解的相邻解中搜索最优解的过程。爬山算法以一定的随机性向搜索空间中的最大值点爬升，能够找到一个局部最优解，但不能保证找到全局最优解。
import random
def hillclimbing(start_state, next_states, objective_fn):
current_state = start_state
current_cost = objective_fn(current_state)
while True:
neighbors = next_states(current_state)
neighbor_costs = [objective_fn(n) for n in neighbors]
if all(nc >= current_cost for nc in neighbor_costs):
return current_state
best_neighbor = neighbors[neighbor_costs.index(max(neighbor_costs))]
if objective_fn(best_neighbor) >= current_cost:
return best_neighbor
current_state = best_neighbor
current_cost = objective_fn(best_neighbor)
遗传算法是一种优化算法，使用领域特定的创新和组合以进一步提高每一代种群的适应性。种群中每个个体都包含了一组潜在解的变量，并以遗传算子进行“交叉”和“变异”，以产生新的个体，从而不断提高种群性能。
import random
def genetic_algorithm(population, fitness_fn, gene_pool, f_thres, max_gens):
best_individual = None
best_fitness = float('-inf')
for i in range(max_gens):
mutated_population = []
for individual in population:
mutated_individual = mutate(individual, gene_pool)
mutated_population.append(mutated_individual)
population = crossover(population, mutated_population)
for i, individual in enumerate(population):
if fitness_fn(individual) > f_thres:
return individual
if fitness_fn(individual) > best_fitness:
best_individual = individual
best_fitness = fitness_fn(individual)
return best_individual
def mutate(individual, gene_pool):
pos_to_mutate = random.randrange(len(individual))
new_gene, alternate = random.sample(gene_pool, 2)
mutated = list(individual)
mutated[pos_to_mutate] = alternate if new_gene == mutated[pos_to_mutate] else new_gene
return tuple(mutated)
def crossover(parents1, parents2):
result = []
for parent1, parent2 in zip(parents1, parents2):
pos = random.randrange(len(parent1))
result.append(parent1[:pos] + parent2[pos:])
return result
人工神经网络是一种类比于人脑的计算机程序，通过架构多层的神经元来模拟人类思维的过程。人工神经网络是一种不断学习和优化的算法，它通过反向传播算法不断调整其权重和偏置值，从而使训练集数据的误差最小化。
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(x):
return x * (1 - x)
class NeuralNetwork:
def __init__(self, layers):
self.layers = layers
self.weights = [np.random.randn(layers[i], layers[i+1]) for i in range(len(layers)-1)]
self.biases = [np.random.randn(layers[i+1]) for i in range(len(layers)-1)]
def feedforward(self, x):
a = x
for w, b in zip(self.weights, self.biases):
z = np.dot(a, w) + b
a = sigmoid(z)
return a
def backpropagation(self, x, y, lr):
zs = []
activations = [x]
a = x
for w, b in zip(self.weights, self.biases):
z = np.dot(a, w) + b
zs.append(z)
a = sigmoid(z)
activations.append(a)
delta = (activations[-1] - y) * sigmoid_derivative(zs[-1])
gradients = [np.dot(activations[-2].reshape((-1, 1)), delta.reshape((1, -1)))]
biases_gradients = [delta]
for i in range(2, len(self.layers)):
delta = np.dot(delta, self.weights[-i+1].T) * sigmoid_derivative(zs[-i])
gradients = [np.dot(activations[-i-1].reshape((-1, 1)), delta.reshape((1, -1)))] + gradients
biases_gradients = [delta] + biases_gradients
self.weights -= lr * np.concatenate(gradients)
self.biases -= lr * np.concatenate(biases_gradients)
def train(self, x_train, y_train, lr, epochs):
for i in range(epochs):
for j in range(len(x_train)):
x = x_train[j]
y = y_train[j]
self.backpropagation(x, y, lr)
def predict(self, x):
prediction = self.feedforward(x)
return np.argmax(prediction)
以上就是常见的人工智能搜索算法的介绍和代码实现，每种算法都有其适用范围和潜在的局限性，具体使用时需要根据问题的特点选择合适的算法。