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Biomaker CA: a Biome Maker project using Cellular Automata

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arxiv 2307.09320 v1 pith:QXPPXJB2 submitted 2023-07-18 cs.AI cs.LGcs.NE

classification cs.AIcs.LGcs.NE
keywords biomakerbiomeenvironmentprojectsurviveautomatabiomescellular
verification ladder T0 review T1 audit T2 compute T3 formal

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We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2D grids and parallelize all of its computation on GPUs through the Python JAX framework. We show how this project allows for several different kinds of environments and laws of 'physics', alongside different model architectures and mutation strategies. We further analyze some configurations to show how plant agents can grow, survive, reproduce, and evolve, forming stable and unstable biomes. We then demonstrate how one can meta-evolve models to survive in a harsh environment either through end-to-end meta-evolution or by a more surgical and efficient approach, called Petri dish meta-evolution. Finally, we show how to perform interactive evolution, where the user decides how to evolve a plant model interactively and then deploys it in a larger environment. We open source Biomaker CA at: https://tinyurl.com/2x8yu34s .

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Cited by 3 Pith papers

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  3. ARC-NCA: Towards Developmental Solutions to the Abstraction and Reasoning Corpus

    cs.AI 2025-05 conditional novelty 5.0 of 10

    ARC-NCA shows that per-task test-time training of Neural Cellular Automata can solve about 13 percent of a 262-task ARC-AGI subset, but the claimed parity with ChatGPT 4.5 relies on results from different benchmark splits.

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