Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity

TL;DR

This paper introduces Leniabreeder, a framework combining Quality-Diversity algorithms with Lenia, a continuous cellular automaton, to automatically discover diverse, lifelike self-organizing patterns, advancing toward open-ended artificial evolution.

Contribution

It presents a novel method that leverages Quality-Diversity algorithms with Lenia to automatically generate diverse, complex self-organizing patterns, moving closer to open-ended evolution in artificial systems.

Findings

Leniabreeder effectively discovers diverse self-organizing patterns.

Unsupervised Quality-Diversity broadens the scope of patterns.

The approach demonstrates sustained diversity and complexity.

Abstract

From the formation of snowflakes to the evolution of diverse life forms, emergence is ubiquitous in our universe. In the quest to understand how complexity can arise from simple rules, abstract computational models, such as cellular automata, have been developed to study self-organization. However, the discovery of self-organizing patterns in artificial systems is challenging and has largely relied on manual or semi-automatic search in the past. In this paper, we show that Quality-Diversity, a family of Evolutionary Algorithms, is an effective framework for the automatic discovery of diverse self-organizing patterns in complex systems. Quality-Diversity algorithms aim to evolve a large population of diverse individuals, each adapted to its ecological niche. Combined with Lenia, a family of continuous cellular automata, we demonstrate that our method is able to evolve a diverse population of lifelike self-organizing autonomous patterns. Our framework, called Leniabreeder, can leverage both manually defined diversity criteria to guide the search toward interesting areas, as well as unsupervised measures of diversity to broaden the scope of discoverable patterns. We demonstrate both qualitatively and quantitatively that Leniabreeder offers a powerful solution for discovering self-organizing patterns. The effectiveness of unsupervised Quality-Diversity methods combined with the rich landscape of Lenia exhibits a sustained generation of diversity and complexity characteristic of biological evolution. We provide empirical evidence that suggests unbounded diversity and argue that Leniabreeder is a step toward replicating open-ended evolution in silico.