Experiment 02
Evolutionary agents
A population of creatures, each with a tiny brain — a neural network — dropped into a world with food, where energy runs out. No line of code tells them to look for food: that "behavior" emerges, generation after generation, from natural selection alone.
What is neuroevolution?
Each agent is driven by a very small neural network: a few dozen to a few hundred weights that turn what the agent "senses" (rays that detect nearby food and other agents, plus its own energy level) into a movement decision. This network was never trained to recognize food, nor programmed to move toward it — at the start of each experiment its weights are drawn at random, and so is its behavior.
What moves the population forward is not the gradient backpropagation used in classical deep learning — it is a genetic algorithm. Each generation, agents are scored on their ability to survive and feed (their "fitness"), the best performers are selected to reproduce — their weights are copied and slightly mutated — and the weakest are removed from the population. No target, no differentiable loss function, no labeled data: just repeated selection, the way evolutionary biology works.
Why this is emergence — and how it differs from Lenia
In Lenia, emergence produces shapes: one local rule, identical everywhere, gives rise to a stable, mobile structure within a few hundred steps. Here, emergence produces behavior: no single agent evolves over the course of one simulation — the entire population changes from generation to generation. The impulse to "seek" food exists in no line of code; it appears because the agents that, through random mutation, happened to steer toward food left more descendants than the rest.
LifeLabs does not claim these agents "want" anything, or that they are aware of the food they pursue: a network with a few dozen weights has no representation of the world comparable to ours. But their behavior satisfies a precise functional criterion — it improves, generation after generation, on a survival task — which is why words like "seek," "avoid," or "learn" appear here in quotes: a descriptive shorthand, not a claim about subjective experience.
Pilot
Replaying a trajectory
The player below replays, frame by frame, the recorded positions of a population of agents and their food over the course of a run. Until a real run is published, it shows a sample trajectory — the replay mechanism itself is identical.
Loading trajectory…
The replay couldn't be loaded
This run's trajectory couldn't be fetched. In the meantime, here's a video capture of the simulation, when available.
The vital signs we measure
Each run records five time series, computed generation after generation, which serve as "vital signs" for the observed population:
- Population
- the number of agents simulated in the current generation — selection and reproduction reshape its composition, rarely the total headcount.
- Mean fitness
- the population's mean fitness — the average score (survival, food found) used to decide who reproduces; a rising mean fitness signals the population is collectively improving at the task.
- Food-approach speed
- the rate at which agents close the distance to the nearest food over an episode — the most direct emergence signal: near zero for a still-random population, it rises as food-seeking behavior takes hold.
- Behavioral diversity
- a measure of how varied behavior is within a single generation — it drops as one dominant strategy takes over the population, and stays high while the genetic algorithm is still exploring multiple approaches.
- Mean energy
- the average energy reserve agents hold at the end of an episode — an indirect measure of efficiency: a population that finishes with energy to spare is feeding better than it spends.
