用遗传编程自动设计卷积神经网络，省去人工调参
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks
- 仅用变异操作的遗传编程生成CNN结构
- 初步实验显示能有效生成高性能网络架构
- 适合对自动化模型设计感兴趣的开发者
本研究提出一种基于笛卡尔遗传编程（Cartesian Genetic Programming, CGP）的神经架构搜索（NAS）方法，用于自动设计和优化卷积神经网络（Convolutional Neural Networks, CNNs）。当前主流网络架构多由人工专家手动设计，过程耗时且易出错。本文采用纯遗传编程方法构建CNN，仅使用单一遗传操作——变异。初步实验表明该方法能生成有潜力的网络结构，为自动化神经网络设计提供新思路。
原文摘要 · Abstract (English)
The present study covers an approach to neural architecture search (NAS) using Cartesian genetic programming (CGP) for the design and optimization of Convolutional Neural Networks (CNNs). In designing artificial neural networks, one crucial aspect of the innovative approach is suggesting a novel neural architecture. Currently used architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In this work, we use pure Genetic Programming Approach to design CNNs, which employs only one genetic operation, i.e., mutation. In the course of preliminary experiments, our methodology yields promising results.
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