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yichuan.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#GA遗传算法
+def rastrigin(x):
+    A = 10
+    n = len(x)
+    return A*n + np.sum(x**2 - A*np.cos(2*np.pi*x))
+
+def create_population(pop_size, n):
+    return np.random.uniform(-5.12, 5.12, size=(pop_size, n))
+
+def evaluate_population(population):
+    return np.array([rastrigin(x) for x in population])
+
+def select_parents(population, fitness, num_parents):
+    sorted_indices = np.argsort(fitness)
+    return population[sorted_indices[:num_parents], :]
+
+def crossover(parents, offspring_size):
+    offspring = np.empty(offspring_size)
+    crossover_point = offspring_size[1] // 2
+
+    for k in range(offspring_size[0]):
+        parent1_idx = k % parents.shape[0]
+        parent2_idx = (k+1) % parents.shape[0]
+        offspring[k, :crossover_point] = parents[parent1_idx, :crossover_point]
+        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
+
+    return offspring
+
+def mutation(offspring_crossover):
+    mutation_rate = 0.01
+    for idx in range(offspring_crossover.shape[0]):
+        for gene_idx in range(offspring_crossover.shape[1]):
+            if np.random.rand() < mutation_rate:
+                offspring_crossover[idx, gene_idx] = np.random.uniform(-5.12, 5.12)
+    return offspring_crossover
+
+def genetic_algorithm(pop_size, n, num_generations):
+    population = create_population(pop_size, n)
+    best_fitness = np.inf
+    best_individual = None
+    best_fitness_record = []
+
+    for i in range(num_generations):
+        fitness = evaluate_population(population)
+        if np.min(fitness) < best_fitness:
+            best_fitness = np.min(fitness)
+            best_individual = population[np.argmin(fitness), :]
+        parents = select_parents(population, fitness, num_parents=2)
+        offspring_crossover = crossover(parents, offspring_size=(pop_size-2, n))
+        offspring_mutation = mutation(offspring_crossover)
+        population[2:, :] = offspring_mutation
+        population[:2, :] = parents
+        best_fitness_record.append(best_fitness)
+    return best_individual, best_fitness_record
+
+best_individual, best_fitness_record = genetic_algorithm(pop_size=50, n=3, num_generations=1000)
+
+plt.plot(best_fitness_record)
+plt.title('Genetic Algorithm for Rastrigin Function')
+plt.xlabel('Generation')
+plt.ylabel('Best Fitness')
+plt.show()