论文标题
Neuroevo：一个基于云的平台，用于使用进化和粒子群算法对神经网络进行自动设计和培训
NeuroEvo: A Cloud-based Platform for Automated Design and Training of Neural Networks using Evolutionary and Particle Swarm Algorithms
论文作者
论文摘要
进化算法（EAS）提供了在复杂搜索空间中优化神经网络的独特优势。本文推出了一个新的Web平台Neuroevo（Neuroevo.io），该平台允许用户使用进化和粒子群算法进行交互设计和训练神经网络分类器。分类问题和培训数据由用户提供，并且在完成培训过程后，最佳分类器可在Python，Java和JavaScript中下载和实施。 Neuroevo是一种基于云的应用程序，它利用GPU并行化以提高独立进化步骤（例如突变，交叉和健身评估）在整个人群中执行的速度。本文概述了用户指定设计决策和超参数设置的培训算法和机会。本文所述的算法也可作为python软件包（pypi：https：//pypi.org/project/project/neuroevo/）提供。
Evolutionary algorithms (EAs) provide unique advantages for optimizing neural networks in complex search spaces. This paper introduces a new web platform, NeuroEvo (neuroevo.io), that allows users to interactively design and train neural network classifiers using evolutionary and particle swarm algorithms. The classification problem and training data are provided by the user and, upon completion of the training process, the best classifier is made available to download and implement in Python, Java, and JavaScript. NeuroEvo is a cloud-based application that leverages GPU parallelization to improve the speed with which the independent evolutionary steps, such as mutation, crossover, and fitness evaluation, are executed across the population. This paper outlines the training algorithms and opportunities for users to specify design decisions and hyperparameter settings. The algorithms described in this paper are also made available as a Python package, neuroevo (PyPI: https://pypi.org/project/neuroevo/).
