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/).
翻译:本文介绍了一个新的网络平台NeuroEvo(neuroevo.io),使用户能够利用进化和粒子群算法对神经网络分类进行互动设计和培训;分类问题和培训数据由用户提供,培训进程完成后,最佳分类程序可在Python、Java和JavaScript下载和实施。NeuroEvo是一种基于云的应用程序,它利用GPU平行功能提高独立演化步骤(例如突变、交叉和健身评价)在人口范围内执行的速度。本文概述了培训算法和用户指定设计决定和超参数设置的机会。本文描述的算法也作为Python软件包、神经(PyPI:https://pypi.org/project/neuroevo/)提供。