In this paper, we introduce a novel method for merging the weights of multiple pre-trained neural networks using a genetic algorithm called MeGA. Traditional techniques, such as weight averaging and ensemble methods, often fail to fully harness the capabilities of pre-trained networks. Our approach leverages a genetic algorithm with tournament selection, crossover, and mutation to optimize weight combinations, creating a more effective fusion. This technique allows the merged model to inherit advantageous features from both parent models, resulting in enhanced accuracy and robustness. Through experiments on the CIFAR-10 dataset, we demonstrate that our genetic algorithm-based weight merging method improves test accuracy compared to individual models and conventional methods. This approach provides a scalable solution for integrating multiple pre-trained networks across various deep learning applications. Github is available at: https://github.com/YUNBLAK/MeGA-Merging-Multiple-Independently-Trained-Neural-Networks-Based-on-Genetic-Algorithm

翻译：本文提出了一种名为MeGA的新方法，利用遗传算法对多个预训练神经网络的权重进行融合。传统方法（如权重平均和集成方法）往往无法充分利用预训练网络的潜力。我们的方法采用带有锦标赛选择、交叉和变异操作的遗传算法来优化权重组合，从而实现更有效的融合。该技术使融合后的模型能够继承来自父模型的优势特征，从而提升准确性与鲁棒性。通过在CIFAR-10数据集上的实验，我们证明了基于遗传算法的权重融合方法相较于单一模型及传统方法，能够提高测试准确率。该方法为整合多个预训练网络提供了一种可扩展的解决方案，适用于各类深度学习应用。项目代码已开源：https://github.com/YUNBLAK/MeGA-Merging-Multiple-Independently-Trained-Neural-Networks-Based-on-Genetic-Algorithm