Deep learning (DL) has become an important machine learning approach that has been widely successful in many applications. Currently, DL is one of the best methods of extracting knowledge from large sets of raw data in a (nearly) self-organized manner. The technical design of DL depends on the feed-forward information flow principle of artificial neural networks with multiple layers of hidden neurons, which form deep neural networks (DNNs). DNNs have various architectures and parameters and are often developed for specific applications. However, the training process of DNNs can be prolonged based on the application and training set size (Gong et al. 2015 ). Moreover, finding the most accurate and efficient architecture of a deep learning system in a reasonable time is a potential difficulty associated with this approach. Swarm intelligence (SI) and evolutionary computing (EC) techniques represent simulation-driven non-convex optimization frameworks with few assumptions based on objective functions. These methods are flexible and have been proven effective in many applications; therefore, they can be used to improve DL by optimizing the applied learning models. This paper presents a comprehensive survey of the most recent approaches involving the hybridization of SI and EC algorithms for DL, the architecture of DNNs, and DNN training to improve the classification accuracy. The paper reviews the significant roles of SI and EC in optimizing the hyper-parameters and architectures of a DL system in context to large scale data analytics. Finally, we identify some open problems for further research, as well as potential issues related to DL that require improvements, and an extensive bibliography of the pertinent research is presented.

中文翻译：

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用于深度学习的群体和进化计算方法的调查

深度学习 (DL) 已成为一种重要的机器学习方法，已在许多应用中取得广泛成功。目前，DL 是以（几乎）自组织的方式从大量原始数据中提取知识的最佳方法之一。DL 的技术设计依赖于具有多层隐藏神经元的人工神经网络的前馈信息流原理，这些神经网络形成了深度神经网络 (DNN)。DNN 具有各种架构和参数，通常是为特定应用而开发的。然而，DNN 的训练过程可以根据应用程序和训练集大小延长（Gong et al. 2015）。此外，在合理的时间内找到最准确和最有效的深度学习系统架构是与这种方法相关的潜在困难。群智能 (SI) 和进化计算 (EC) 技术代表模拟驱动的非凸优化框架，基于目标函数的假设很少。这些方法很灵活，并且在许多应用中都被证明是有效的；因此，它们可以通过优化应用的学习模型来改进深度学习。本文对最新的方法进行了全面调查，这些方法涉及 DL 的 SI 和 EC 算法的混合、DNN 的架构和 DNN 训练，以提高分类精度。该论文回顾了 SI 和 EC 在优化 DL 系统的超参数和架构方面的重要作用，以适应大规模数据分析。最后，我们确定了一些有待进一步研究的开放性问题，以及与 DL 相关的需要改进的潜在问题，