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

arXiv: https://arxiv.org/abs/2406.04235 · HTML: https://mlsanity.com/paper/2406.04235/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 12 of 12)

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- [AutomataGPT: Forecasting and Ruleset Inference for Two-Dimensional Cellular Automata](https://mlsanity.com/paper/2506.17333/) (2025)
- [Discovering Quality-Diversity Algorithms via Meta-Black-Box Optimization](https://mlsanity.com/paper/2502.02190/) (2025)
- [Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics](https://mlsanity.com/paper/2505.15998/) (2025)
- [Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata](https://mlsanity.com/paper/2509.03863/) (2025)
- [Guiding Evolution of Artificial Life Using Vision-Language Models](https://mlsanity.com/paper/2509.22447/) (2025)
- [Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training](https://mlsanity.com/paper/2604.11248/) (2026)
- [The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning](https://mlsanity.com/paper/2608.26116/) (2026)

## Abstract (arXiv)

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.