# ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm

arXiv: https://arxiv.org/abs/2308.11522 · HTML: https://mlsanity.com/paper/2308.11522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 87 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Reservoir Computing using Cellular Automata](https://mlsanity.com/paper/1410.0162/) (2014)
- [Reservoir Computing Using Non-Uniform Binary Cellular Automata](https://mlsanity.com/paper/1702.03812/) (2017)

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

- [On when is Reservoir Computing with Cellular Automata Beneficial?](https://mlsanity.com/paper/2407.09501/) (2024)
- [A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks](https://mlsanity.com/paper/2407.18017/) (2024)

## Abstract (arXiv)

In this paper, we present a novel algorithm to optimize the design of Reservoir Computing using Cellular Automata models for time series applications. Besides selecting the models' hyperparameters, the proposed algorithm particularly solves the open problem of linear Cellular Automaton rule selection. The selection method pre-selects only a few promising candidate rules out of an exponentially growing rule space. When applied to relevant benchmark datasets, the selected rules achieve low errors, with the best rules being among the top 5% of the overall rule space. The algorithm was developed based on mathematical analysis of linear Cellular Automaton properties and is backed by almost one million experiments, adding up to a computational runtime of nearly one year. Comparisons to other state-of-the-art time series models show that the proposed Reservoir Computing using Cellular Automata models have lower computational complexity, at the same time, achieve lower errors. Hence, our approach reduces the time needed for training and hyperparameter optimization by up to several orders of magnitude.