Evolving Many Worlds: Towards Open-Ended Discovery in Petri Dish NCA via Population-Based Training
The generation of sustained, open-ended complexity from local interactions remains a fundamental challenge in artificial life. Differentiable multi-agent systems, such as Petri Dish Neural Cellular Automata (PD-NCA), exhibit rich self-organization driven purely by spatial competition; however, they are highly sensitive to hyperparameters and frequently collapse into uninteresting patterns and dynamics, such as frozen equilibria or structureless noise. In this paper, we introduce PBT-NCA, a meta-evolutionary algorithm that evolves a population of PD-NCAs subject to a composite objective that rewards both historical behavioral novelty and contemporary visual diversity. Driven by this contin...
| Authors | Uljad Berdica, Jakob Foerster, Frank Hutter et al. |
|---|---|
| Primary category | cs.NE |
| Published | 13 April 2026 |
| Last updated at source | 22 June 2026 |
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