With @hamongautier.bsky.social , Mayelen Etcheverry, @bertchakovsky.bsky.social , @pyoudeyer.bsky.social and @clemmoulinfrier.bsky.social
Go check our paper for more !
📄 doi.org/10.1162/artl...
📄 www.arxiv.org/abs/2506.08569

And the optimization of the simulation parameters towards desired objectives, e.g creatures sensing and climbing gradients :

This work also recapitulates previous results with the Flow-Lenia system Like the discovery of complex emergent creatures form random search only:

While we observe evolutionary dynamics in the system they still tend to settle and produce bounded complexity.
The quest for open-ended dynamics is then still on ! In your opinion what is lacking now for more complexity to evolve ?

1. A dissipative environment with constant in- and out-flows of matter(=energy)
2. An environment where creatures need to consume, and compete for, external resources in order to counter their inevitable dissipation.

What if we could then engineer synthetic ecosystems to test our theories on the emergence of life and the evolution of complexity ?
That’s what we propose by designing 2 additional environments within FL and measure their effects on the long-term behaviour of the system:

We use these metrics to evaluate the evolutionary dynamics e$merging from Flow-Lenia local rules in different conditions and also visualise emerging evolutionary trees:

We go larger scales in this work by simulating larger environments for longer ! We then use evolutionary activity metrics (see tinyurl.com/3chv37m9), a framework designed to analyse systems that evolve under intrinsic fitnesses, to evaluate the long-term evolutionary dynamics

FL allows to embed the CA parameters within its dynamics, making them localised, just like genomes ! This makes FL a unique system to simulate multi-species environments where rules can compete for mass, spread and undergo mutations: all the ingredients for evolution to emerge !