Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks. A popular subclass of neuroevolutionary methods, called evolution strategies, relies on dense noise perturbations to mutate networks, which can be sample inefficient and challenging for large models with millions of parameters. We introduce an approach to alleviating this problem by decomposing dense mutations into low-dimensional subspaces. Restricting mutations in this way can significantly reduce variance as networks can handle stronger perturbations while maintaining performance, which enables a more controlled and targeted evolution of deep networks. This approach is uniquely effective for the task of fine tuning pre-trained models, which is an increasingly valuable area of research as networks continue to scale in size and open source models become more widely available. Furthermore, we show how this work naturally connects to ensemble learning where sparse mutations encourage diversity among children such that their combined predictions can reliably improve performance. We conduct the first large scale exploration of neuroevolutionary fine tuning and ensembling on the notoriously difficult ImageNet dataset, where we see small generalization improvements with only a single evolutionary generation using nearly a dozen different deep neural network architectures.
翻译:神经进化是将进化算法与神经网络相结合的一个充满希望的研究领域。一个流行的神经进化方法亚类,称为进化战略,依靠密集的噪声扰动到变异网络中,这些网络的取样效率低,对具有数以百万计参数的大型模型来说具有挑战性。我们引入了一种缓解这一问题的方法,将稠密的突变分解成低维次空间。以这种方式限制突变可以大大减少差异,因为网络既能处理更强的扰动,又能保持性能,从而能够使深层网络更受控制和有针对性地演变。这一方法对精细调整预先训练模型的任务来说是独特的有效。随着网络在规模上继续扩大和开放源模型的普及,这是一个越来越有价值的研究领域。此外,我们展示了这项工作如何自然地连接到共同学习,而零散的变异能会鼓励儿童之间的多样性,从而使他们的综合预测能够可靠地改善性能。我们第一次大规模探索神经进化微调整和聚合出臭名难的图像网集成。我们在这里看到的是小的普通化改进,只有使用近十几组不同的进化型的单一进化网络结构。