Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the trained readout layer. However, classical Echo State Networks (ESNs) often require task-specific tuning of their architecture and hyperparameters to achieve good performance. This paper introduces EARLY (Evolutionary Algorithm for Reservoir Learning and Yielding), a framework designed to evolve both the topology and hyperparameters of multi-reservoir ESNs. Inspired by the modular organisation of the brain, EARLY encodes architectures as graph-based genomes and applies crossover, mutation, and selection to discover effective configurations. Our goal is to create both generic architectures and tasks inducing generalization. The method is evaluated on temporal learning tasks from the CogScale dataset. Results show that evolved architectures outperform those obtained with random search on several tasks and exhibit structural differences depending on task difficulty: simpler tasks yield lightweight architectures, while more complex tasks favour richer modular organisations. These findings suggest that evolutionary search can help identify reusable reservoir structures for a broader range of temporal problems. The evolved architectures are further evaluated on a cross-situational learning dataset to assess their ability to adapt to new environments.

翻译：储层计算作为一种递归神经网络，因其将动态处理与训练后的读出层分离而成为时序学习领域中极具前景的方法。然而，经典回声状态网络（ESNs）通常需要针对特定任务调整其架构与超参数，方能实现良好性能。本文提出EARLY（储层学习与生成的进化算法）框架，旨在同时进化多储层ESN的拓扑结构与超参数。受大脑模块化组织启发，EARLY将架构编码为基于图的基因组，并通过交叉、变异与选择操作发现有效配置。我们的目标是创建通用架构与任务诱导的泛化能力。该方法在CogScale数据集的时序学习任务上进行了评估。结果表明，进化获得的架构在多个任务上优于随机搜索得到的架构，并根据任务难度呈现结构差异：简单任务产生轻量级架构，而复杂任务则更倾向于丰富的模块化组织。这些发现表明，进化搜索有助于识别可复用的储层结构，以应对更广泛的时序问题。进一步在跨情境学习数据集上评估进化架构，以验证其适应新环境的能力。