Artificial Life for Science

Description

Artificial Life draws inspiration from natural systems — evolution, development, ecology, collective behaviour — and explores life-as-it-could-be to understand how complexity and intelligence emerge. Yet as a field, it rarely feeds back into the real-world systems that inspired it. This disconnect represents a missed opportunity: ALife has the potential to transform how we model and explore the space of solutions to technical challenges.

In particularly we are interested in how works in our community can be used:

As a modeling paradigm: ALife models capture emergent, non-equilibrium, and self-organising dynamics that traditional approaches often miss. Combined with modern machine learning, they enable data-driven calibration of realistic, interpretable simulations of complex phenomena and can have important implications in bioengineering and synthetic biology [Gorochowski et al., 2020; Zarkesh et al., 2022], and origins of life and astrobiology [Rasmussen et al., 2003; Scharf et al., 2015; Solé et al., 2025].

As methods for engineering bio-inspired solutions: bio-inspired mechanisms can address key engineering challenges such as energy and sample efficiency, robustness to perturbations, and fast, event-driven responsiveness. This is an active area of research in communities such as neuromorphic engineering [Muir & Sheik, 2025] and evolvable hardware [Whitley et al.], synthetic biology [Stock & Gorochowski, 2024], decentralized control and swarm-intelligence [Olfati-Saber et al., 2007], or unconventional and cellular computing [Grozinger et al., 2019] that ALife should more actively engage with.

As a tool for assisting scientific discovery: Research in intrinsic motivation, evolutionary search, and open-endedness can offer tools and theories for assisting human discovery, for example by automating search in open-ended spaces [Reinke et al., 2020] and helping improve our collective innovation abilities [Stanley & Lehman, 2015]. The importance of mechanisms for open-endedness is becoming increasingly evident in our era of Generative AI, where such ideas are being incorporated into tools for automating scientific discovery [Cui et al., 2021; Lange et al., 2025].

Call for Papers

Our session aims at bringing together researchers interested in applying ALife to real-world problems and scientists and industry practitioners curious about how ALife can be helpful in their domains.

We welcome different types of contributions, including:

Reviews and perspectives on existing real-world applications of artificial life, or proposals for new promising areas and long-term visions
Novel computational models motivated by questions stemming from specific fields or bridging communities
Real-world transfer of existing models and techniques (e.g., in the form of tools, applications, games, or human- and societal studies)

The breadth of questions is vast, but here are some we find particularly exciting today, though we particularly invite contributions that go beyond them:

How can self-organising systems (such as Neural Cellular Automata or Diffusion Models) be used as a data-driven modeling paradigm in biology?
How can bio-inspired mechanisms lead to more life-like (robust, energy-efficient, adaptive, smart) human-engineered systems (e.g., in hardware and software)?
How can we access and technologically afford the intelligence already baked into living matter (from subcellular to multicellular regulation to networks, organisms, and beyond)?
How can we design tools for assisting search in open-ended spaces (such as scientific discovery)?
How can we design tools for engineering ecosystems (from natural ones [Maull et al., 2026] to the emerging landscape of Large Language Models [Nisioti et al., 2024])?

Practical Info

Submission deadline: March 30, 2026
How to submit: Conference submission portal
Venue: Hybrid conference with physical venue in Waterloo, Canada
Conference dates: August 17–21, 2026

References

[1] Cui, C., Wang, W., Zhang, M., Chen, G., Luo, Z., & Ooi, B. C. (2021). AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment. In Proceedings of the 2021 International Conference on Management of Data (pp. 2208-2216). https://doi.org/10.1145/3448016.3457324

[2] Grozinger, L., et al. (2019). Pathways to cellular supremacy in biocomputing. Nature Communications, 10(1), 5250. https://doi.org/10.1038/s41467-019-13232-z

[3] Lange, R. T., Imajuku, Y., & Cetin, E. (2025). ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution. arXiv preprint arXiv:2509.19349. https://doi.org/10.48550/arXiv.2509.19349

[4] Maull, V., Shpilkina, Y., de Lorenzo, V., & Solé, R. (2026). Emergent Bioengineering. Preprints. https://doi.org/10.20944/preprints202603.0448.v1

[5] Muir, D. R., & Sheik, S. (2025). The road to commercial success for neuromorphic technologies. Nature Communications, 16(1), 3586. https://doi.org/10.1038/s41467-025-57352-1

[6] Nisioti, E., et al. (2024). From text to life: On the reciprocal relationship between artificial life and large language models. In Artificial Life Conference Proceedings 36 (p. 39). MIT Press. https://direct.mit.edu/isal/proceedings-abstract/isal2024/36/39/123488

[7] Reinke, C., Etcheverry, M., & Oudeyer, P.-Y. (2020). Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems. arXiv preprint arXiv:1908.06663. https://doi.org/10.48550/arXiv.1908.06663

[8] Stanley, K. O., & Lehman, J. (2015). Why Greatness Cannot Be Planned: The Myth of the Objective. Springer International Publishing. https://doi.org/10.1007/978-3-319-15524-1

[9] Whitley, D., Yoder, J., & Carpenter, N. Resurrecting FPGA Intrinsic Analog Evolvable Hardware.

[10] Zarkesh, I., Kazemi Ashtiani, M., Shiri, Z., Aran, S., Braun, T., & Baharvand, H. (2022). Synthetic developmental biology: Engineering approaches to guide multicellular organization. Stem Cell Reports, 17(4), 715-733. https://doi.org/10.1016/j.stemcr.2022.02.004

[11] Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215-233.