Flow-Lenia: Emergent Evolutionary Dynamics in Mass Conservative Continuous Cellular Automata.

2025-04-28

Artificial Life

AI Summary

Emergence of self-organization, self-replication, and open-ended evolution remains a fundamental challenge in artificial life systems. Method: We propose Flow-Lenia, a mass-conserving continuous cellular automaton model wherein evolvable parameters are explicitly embedded within the system’s dynamical equations—enabling endogenous evolution of model properties. Strict mass conservation and spatial locality ensure stable formation of structurally complex, life-like patterns (SLPs). The model supports multi-species coexistence and competition, enabling quantitative analysis of evolutionary activity. Contribution/Results: Experiments demonstrate spontaneous emergence of self-replication, multistability, and biologically plausible evolutionary dynamics—including mutation, selection, and adaptive drift. Flow-Lenia establishes a computationally tractable and interpretable paradigm for open-ended artificial evolution, bridging formal dynamical systems theory with synthetic biology-inspired design principles.

Technology Category

• Machine Learning: Evolutionary Learning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Search and Optimization: Evolutionary Computation

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call "creatures." We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.

Problem

• Extend Lenia to mass-conservative continuous cellular automata
• Generate life-like patterns with complex behaviors
• Study emergent evolutionary dynamics in multispecies simulations

Innovation

• Mass conservative extension of Lenia
• Optimized update rule parameters
• Embedded parameters for multispecies simulations