In this paper, we propose a hybrid model combining genetic algorithm and hill climbing algorithm for optimizing Convolutional Neural Networks (CNNs) on the CIFAR-100 dataset. The proposed model utilizes a population of chromosomes that represent the hyperparameters of the CNN model. The genetic algorithm is used for selecting and breeding the fittest chromosomes to generate new offspring. The hill climbing algorithm is then applied to the offspring to further optimize their hyperparameters. The mutation operation is introduced to diversify the population and to prevent the algorithm from getting stuck in local optima. The Genetic Algorithm is used for global search and exploration of the search space, while Hill Climbing is used for local optimization of promising solutions. The objective function is the accuracy of the trained neural network on the CIFAR-100 test set. The performance of the hybrid model is evaluated by comparing it with the standard genetic algorithm and hill-climbing algorithm. The experimental results demonstrate that the proposed hybrid model achieves better accuracy with fewer generations compared to the standard algorithms. Therefore, the proposed hybrid model can be a promising approach for optimizing CNN models on large datasets.

翻译：本文提出一种融合遗传算法与爬山算法的混合模型，用于在CIFAR-100数据集上优化卷积神经网络（CNN）。该模型采用由代表CNN超参数的染色体构成的种群，通过遗传算法筛选和培育最优染色体以生成新子代，随后对子代应用爬山算法进一步优化其超参数。引入变异操作以增加种群多样性，防止算法陷入局部最优。遗传算法负责搜索空间的全局探索与全局搜索，而爬山算法则针对有潜力的解进行局部优化。目标函数为CIFAR-100测试集上训练后神经网络的准确率。通过与标准遗传算法和爬山算法的对比实验，验证了混合模型的性能。实验结果表明，该混合模型在更少代数内即可获得优于标准算法的准确率。因此，本文提出的混合模型为大规模数据集上CNN模型的优化提供了具有前景的解决路径。