According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super-network. We present the first approach based on a genetic algorithm to find such strong lottery ticket sub-networks without training or otherwise computing any gradient. We show that, for smaller instances of binary classification tasks, our evolutionary approach even produces smaller and better-performing lottery ticket networks than the state-of-the-art approach using gradient information.

翻译：根据强彩票假设，每个具有随机初始化权重的足够大的神经网络都包含一个子网络，该子网络仅凭其随机权重即可在给定任务上达到与训练后的超网络相当的性能。我们提出了首个基于遗传算法的方法，无需训练或计算任何梯度即可找到此类强彩票子网络。研究表明，在较小的二分类任务实例中，我们的进化方法甚至能比使用梯度信息的最先进方法产生更小且性能更优的彩票网络。