Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning
<|reference_start|>Massive Dimensions Reduction and Hybridization with Meta-heuristics in Deep Learning: Deep learning is mainly based on utilizing gradient-based optimization for training Deep Neural Network (DNN) models. Although robust and widely used, gradient-based optimization algorithms are prone to getting stuck in local minima. In this modern deep learning era, the state-of-the-art DNN models have millions and billions of parameters, including weights and biases, making them huge-scale optimization problems in terms of search space. Tuning a huge number of parameters is a challenging task that causes vanishing/exploding gradients and overfitting; likewise, utilized loss functions do not exactly represent our targeted performance metrics. A practical solution to exploring large and complex solution space is meta-heuristic algorithms. Since DNNs exceed thousands and millions of parameters, even robust meta-heuristic algorithms, such as Differential Evolution, struggle to efficiently explore and converge in such huge-dimensional search spaces, leading to very slow convergence and high memory demand. To tackle the mentioned curse of dimensionality, the concept of blocking was recently proposed as a technique that reduces the search space dimensions by grouping them into blocks. In this study, we aim to introduce Histogram-based Blocking Differential Evolution (HBDE), a novel approach that hybridizes gradient-based and gradient-free algorithms to optimize parameters. Experimental results demonstrated that the HBDE could reduce the parameters in the ResNet-18 model from 11M to 3K during the training/optimizing phase by metaheuristics, namely, the proposed HBDE, which outperforms baseline gradient-based and parent gradient-free DE algorithms evaluated on CIFAR-10 and CIFAR-100 datasets showcasing its effectiveness with reduced computational demands for the very first time.<|reference_end|>
@article{khosrowshahli2024massive,
    title={Massive Dimensions Reduction and Hybridization with Meta-heuristics in
  Deep Learning},
    author={Rasa Khosrowshahli, Shahryar Rahnamayan, Beatrice Ombuki-Berman},
    journal={arXiv preprint arXiv:2408.07194},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07194},
    primaryClass={cs.NE cs.AI cs.LG}
}