https://doi.org/10.1140/epjs/s11734-026-02382-z
Regular Article
Information-driven evolution in energy-constrained cellular automata
School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, China
a
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Received:
26
January
2026
Accepted:
9
May
2026
Published online:
22
May
2026
Abstract
Living systems operate far from thermodynamic equilibrium and depend on continuous local information processing. Such processing, however, carries an energetic cost. In this work, we introduce a two-dimensional cellular automaton in which each cell contains an adaptive agent with a minimal internal controller governing local state transitions. Information processing is directly coupled to metabolism by assigning an energy cost proportional to the Shannon entropy of the controller’s internal states. Survival and reproduction depend only on energetic balance and homeostatic stability, with no externally defined fitness function. Long-term simulations show a clear evolutionary shift toward low-entropy and stable control regimes. This indicates selection for energetically efficient information processing rather than for increasing internal complexity. Cells with higher informational variability exhibit shorter lifespans, revealing an entropy–longevity trade-off. At the population level, homeostatic tolerance emerges as a dominant constraint. Meanwhile, the interaction between local resource consumption and regeneration produces persistent spatial patterns and dynamic energy niches across the lattice. Although highly abstract, the model provides a minimal and physically grounded demonstration of how structured organization can arise in cellular automata from the coupling between information regulation and energetic constraints. It offers a simple framework for studying energy–information trade-offs in adaptive systems.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
