不演化的遗传算法（Non-evolutionary Genetic Algorithm）是一种优化算法，它通过模拟自然界的遗传机制来解决问题。在图像重建问题中，可以使用不演化的遗传算法来恢复缺失的像素值。

以下是一个使用不演化的遗传算法进行图像重建的代码示例：

import numpy as np
import random

def non_evolutionary_genetic_algorithm(image, missing_pixels, population_size=100, num_generations=100):
    population = initialize_population(population_size, missing_pixels)
    
    for generation in range(num_generations):
        fitness_scores = evaluate_fitness(image, missing_pixels, population)
        best_individual = population[np.argmax(fitness_scores)]
        
        print("Generation:", generation+1, "Best Fitness:", np.max(fitness_scores))
        
        population = select_best_individuals(population, fitness_scores)
        population = crossover(population)
        population = mutate(population)
        population = np.concatenate([population, best_individual.reshape(1, -1)])
    
    best_individual = population[np.argmax(fitness_scores)]
    reconstructed_image = image.copy()
    reconstructed_image[missing_pixels] = best_individual
    
    return reconstructed_image

def initialize_population(population_size, missing_pixels):
    population = np.zeros((population_size, len(missing_pixels)), dtype=int)
    
    for i in range(population_size):
        population[i] = np.random.randint(0, 256, len(missing_pixels))
    
    return population

def evaluate_fitness(image, missing_pixels, population):
    fitness_scores = np.zeros(len(population))
    
    for i in range(len(population)):
        reconstructed_image = image.copy()
        reconstructed_image[missing_pixels] = population[i]
        squared_diff = (reconstructed_image - image)**2
        fitness_scores[i] = -np.sum(squared_diff)
    
    return fitness_scores

def select_best_individuals(population, fitness_scores, num_best=50):
    sorted_indices = np.argsort(fitness_scores)[::-1]
    best_individuals = population[sorted_indices[:num_best]]
    
    return best_individuals

def crossover(population, num_offsprings=50):
    offsprings = np.zeros((num_offsprings, population.shape[1]), dtype=int)
    
    for i in range(num_offsprings):
        parent1, parent2 = random.sample(range(len(population)), 2)
        crossover_point = random.randint(1, population.shape[1]-1)
        offsprings[i, :crossover_point] = population[parent1, :crossover_point]
        offsprings[i, crossover_point:] = population[parent2, crossover_point:]
    
    return offsprings

def mutate(population, mutation_rate=0.01):
    for i in range(len(population)):
        for j in range(population.shape[1]):
            if random.random() < mutation_rate:
                population[i, j] = random.randint(0, 256)
    
    return population

# 示例用法
image = np.random.randint(0, 256, (10, 10))
missing_pixels = [(1, 2), (3, 4), (5, 6)]
reconstructed_image = non_evolutionary_genetic_algorithm(image, missing_pixels)

上述代码中，image表示原始图像，missing_pixels表示缺失像素的坐标。non_evolutionary_genetic_algorithm函数是主要的图像重建函数，它通过不演化的遗传算法来逐步恢复缺失的像素值。

首先，使用initialize_population函数初始化一个由随机像素值组成的种群。然后，通过遗传算法的选择、交叉和变异操作，逐代进行种群更新。在每一代中，根据适应度评估函数evaluate_fitness计算种群中每个个体的适应度得分，选择最好的个体作为本代的最佳个体。最终，将最佳个体的像素值赋给缺失像素，得到重建的图像。

这只是一个简单的示例，实际的图像重建问题可能需要更复杂的算法和技巧。此外，可能需要进行参数调整和优化以获得更好的重建结果。