MeGA: Merging Multiple Independently Trained Neural Networks Based on Genetic Algorithm
<|reference_start|>MeGA: Merging Multiple Independently Trained Neural Networks Based on Genetic Algorithm: 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<|reference_end|>
@article{yun2024mega:,
    title={MeGA: Merging Multiple Independently Trained Neural Networks Based on
  Genetic Algorithm},
    author={Daniel Yun},
    journal={arXiv preprint arXiv:2406.04607},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04607},
    primaryClass={cs.NE cs.AI cs.LG}
}