Evolutionary Algorithm for Reservoir Learning and Yielding
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 ...
| Authors | Julien Testu, Pierrick Legrand, Xavier Hinaut |
|---|---|
| Primary category | cs.NE |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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