论文标题
带有细胞编码的基因表达编程的进化NA
Evolutionary NAS with Gene Expression Programming of Cellular Encoding
论文作者
论文摘要
神经结构搜索（NAS）的复兴已经看到了经典方法，例如遗传算法（GA）和遗传编程（GP）用于卷积神经网络（CNN）架构。尽管最近的工作在视觉感知任务上实现了有希望的表现，但GA和GP的直接编码方案都具有功能复杂性缺陷，并且在CNN等大型体系结构上不能很好地扩展。为了解决这个问题，我们提出了一种新的生成编码方案 - $符号\ linear \ linear \生成\编码$（SLGE） - 简单但功能强大的方案，该方案嵌入了线性固定长度字符串染色体中的本地图转换，以开发CNN架构的CNN架构，并通过Gene表达式编程的进化过程来开发变异形状的CNN架构。在实验中，发现SLGE的有效性在发现架构上可以改善CIFAR-10和CIFAR-100图像分类任务上最先进的手工CNN体系结构的性能；并使用较少的GPU资源通过现有的NAS方法实现竞争性分类错误率。
The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme -- $symbolic\ linear\ generative\ encoding$ (SLGE) -- simple, yet powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the state-of-the-art handcrafted CNN architectures on CIFAR-10 and CIFAR-100 image classification tasks; and achieves a competitive classification error rate with the existing NAS methods using less GPU resources.
