Visualizing the Structure of Lenia Parameter Space

TL;DR

This paper introduces a new classification method for Lenia, a continuous cellular automaton, revealing the structure of its parameter space and discovering new soliton families and universal phase space features.

Contribution

A novel automatic classification approach for Lenia systems that visualizes parameter space and uncovers new soliton families and universal structures.

Findings

Detection of new soliton families

Visualization of Lenia's parameter space structure

Universal phase space features across kernels

Abstract

Continuous cellular automata are rocketing in popularity, yet developing a theoretical understanding of their behaviour remains a challenge. In the case of Lenia, a few fundamental open problems include determining what exactly constitutes a soliton, what is the overall structure of the parameter space, and where do the solitons occur in it. In this abstract, we present a new method to automatically classify Lenia systems into four qualitatively different dynamical classes. This allows us to detect moving solitons, and to provide an interactive visualization of Lenia's parameter space structure on our website https://lenia-explorer.vercel.app/. The results shed new light on the above-mentioned questions and lead to several observations: the existence of new soliton families for parameters where they were not believed to exist, or the universality of the phase space structure across various kernels.