# Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms

arXiv: https://arxiv.org/abs/2211.06254 · HTML: https://mlsanity.com/paper/2211.06254/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Deep Echo State Network (DeepESN): A Brief Survey](https://mlsanity.com/paper/1712.04323/) (2017)
- [A Review of Designs and Applications of Echo State Networks](https://mlsanity.com/paper/2012.02974/) (2020)
- [Efficient Optimization of Echo State Networks for Time Series Datasets](https://mlsanity.com/paper/1903.05071/) (2019)
- [Reservoir Topology in Deep Echo State Networks](https://mlsanity.com/paper/1909.11022/) (2019)
- [An Experimental Analysis of the Echo State Network Initialization Using the Particle Swarm Optimization](https://mlsanity.com/paper/1501.00436/) (2015)
- [Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property](https://mlsanity.com/paper/1703.06664/) (2017)
- [Neuroevolution on the Edge of Chaos](https://mlsanity.com/paper/1706.01330/) (2017)
- [Evolutionary Echo State Network: evolving reservoirs in the Fourier space](https://mlsanity.com/paper/2206.04951/) (2022)

## Built on by (most cited first, 2 of 2)

- [Efficient Optimisation of Physical Reservoir Computers using only a Delayed Input](https://mlsanity.com/paper/2401.14371/) (2024)
- [Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos](https://mlsanity.com/paper/2606.22765/) (2026)

## Abstract (arXiv)

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 textitreservoir, 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.