论文标题
通过进化算法优化的重新访问储层计算体系结构
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
论文作者
论文摘要
多年来，进化算法（EAS）已应用于改善神经网络（NNS）体系结构。它们已用于解决不同的问题，例如训练网络（调整权重），设计网络拓扑，优化全局参数和选择功能。在这里，我们提供了有关EAS在名为Reservoir Computing（RC）的Recurrent NNS的特定域上应用的系统简要调查。在2000年代初，RC范式似乎是采用经常性NNS的好选择，而无需处理培训算法的不便。 RC模型使用非线性动态系统，其固定的复发神经网络名为\ textit {Reservoir}，并且学习过程仅限于调整线性参数函数。 ％因此，学习的表现快速而精确。但是，RC模型具有多个超参数，因此EAS是找出最佳RC体系结构的有用工具。我们概述了该领域的结果，讨论新的进步，并介绍了有关新趋势和仍在开放问题的愿景。
For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training the networks (adjusting the weights), designing network topology, optimizing global parameters, and selecting features. Here, we provide a systematic brief survey about applications of the EAs on the specific domain of the recurrent NNs named Reservoir Computing (RC). At the beginning of the 2000s, the RC paradigm appeared as a good option for employing recurrent NNs without dealing with the inconveniences of the training algorithms. RC models use a nonlinear dynamic system, with fixed recurrent neural network named the \textit{reservoir}, and learning process is restricted to adjusting a linear parametric function. %so the performance of learning is fast and precise. However, an RC model has several hyper-parameters, therefore EAs are helpful tools to figure out optimal RC architectures. We provide an overview of the results on the area, discuss novel advances, and we present our vision regarding the new trends and still open questions.
