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.

本文提出了一种混合模型，结合了遗传算法和爬山算法，用于优化 CIFAR-100 数据集上的卷积神经网络。实验结果表明，该混合模型比标准算法在更少的迭代次数下实现了更好的准确度，因此该模型可以成为优化大型数据集上的卷积神经网络的一种有前景的方法。