Prebiotic Functional Programs: Endogenous Selection In An Artificial Chemistry

Artificial chemistry simulations produce many intriguing emergent behaviors, but they are often difficult to steer or control.

This paper proposes a method for steering the dynamics of a classic artificial chemistry model, known as AlChemy (Algorithmic Chemistry), which is based on untyped lambda calculus.

Our approach leverages features that are endogenous to AlChemy without constructing an explicit external fitness function or building learning into the dynamics. We demonstrate the approach by synthesizing non-trivial lambda functions, such as Church addition and succession, from simple primitives.

The results provide insight into the possibility of endogenous selection in diverse systems such as autocatalytic chemical networks and software systems.

Devansh Vimal, Cole Mathis, Westley Weimer, Stephanie Forrest

Subjects: Formal Languages and Automata Theory (cs.FL); Populations and Evolution (q-bio.PE)
Cite as: arXiv:2509.03534 [cs.FL] (or arXiv:2509.03534v1 [cs.FL] for this version)
https://doi.org/10.48550/arXiv.2509.03534
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Submission history
From: Cole Mathis
[v1] Wed, 27 Aug 2025 00:01:42 UTC (650 KB)
https://arxiv.org/abs/2509.03534

Astrobiology,