ALIFE History
A Timeline of Artificial Life

About 80 years of asking "what is life?" by building it and watching what happens.

Published October 17, 2026 · About 10 minutes to read · By hamzWorkerz

Artificial Life, usually shortened to ALife (and ALIFE for the conference series), was named as a research field in 1987. But the road to that moment starts much earlier, in the 1940s, when computers themselves were new. This article follows that road in order: where the ideas came from, which models defined each era, and how the field has changed. Most of the models mentioned here can be run in this collection. Open them as you read and play with the ones that catch your interest.

What ALife is trying to do

Biology studies the life that actually exists on Earth. ALife takes one step further and also asks about "life as it could be." The idea is to build systems that show lifelike behavior, in software, in robots, or in chemistry, and to learn what lifelikeness depends on by constructing it. Christopher Langton, who organized the field's founding workshop in 1987, framed it as the counterpart of "life as we know it."

The work is often grouped by medium:

• Soft (software): lifelike systems running inside a computer. This collection is about this branch.
• Hard (robotics): physical robots that behave like living things.
• Wet (chemistry and biochemistry): lifelike processes built in the lab.

There is also a long-running disagreement about what these systems are. The "weak ALife" view says that a program is only ever a model of life. The "strong ALife" view says that a system which meets the right conditions would be alive in its own right. That question is still open. The field has carried on building and testing regardless, which is part of what makes it interesting.

Before ALife (1940s–60s): self-replication and pattern formation

Von Neumann and Ulam: a machine that copies itself

In the 1940s the mathematician John von Neumann was working on a question: could a machine, in principle, build a copy of itself? Around the same time, Stanisław Ulam suggested dividing space into a grid in which each cell decides its next state from its neighbors alone. Von Neumann used this idea to show a design for a self-reproducing machine inside a cellular automaton with 29 states. It was an early demonstration that a defining property of life, self-replication, can be described entirely in terms of information and rules. His work was completed and edited by Arthur Burks and published after his death, in 1966. In 1968 Edgar Codd showed a simpler self-replicating design that needed only 8 states.

Cybernetics and the robot tortoises

In 1948 Norbert Wiener published Cybernetics, which popularized talking about animals and machines in the same language of information and feedback. At about the same time, William Grey Walter built small tortoise-like robots that moved toward light and returned to a charging station when their batteries ran low (around 1948–49). Lifelike behavior from very simple circuitry is an early ancestor of what we now call hard ALife. You can recreate its behavior in the Grey Walter’s tortoise simulator.

Turing: where patterns come from

In 1952 Alan Turing published "The Chemical Basis of Morphogenesis." He showed that two chemicals that react and diffuse can spontaneously form stripes and spots. It was one of the first mathematical attempts to explain how animal markings arise. The idea leads on to the Gray–Scott model (1984) and to Pearson's classification of its patterns (1993). The Gray-Scott simulator in this collection belongs to that line.

Other early threads

• Predators and prey: in the 1920s Lotka and Volterra described, with equations, how predator and prey populations rise and fall in cycles. The Predator–Prey simulator runs the same idea with individual agents.
• L-systems: in 1968 Aristid Lindenmayer proposed describing plant branching with string-rewriting rules. Try it in the L-system simulator.

1970s–80s: lifelike toys you can play with

1970: the Game of Life

In 1970 John Conway's Game of Life reached a wide audience through Martin Gardner's column in Scientific American. Every cell is either alive or dead, and its next state depends only on how many of its eight neighbors are alive. From that, you get gliders that travel across the grid, blinkers that flash with a fixed period, and even "guns" that keep producing new patterns. Very simple rules can produce surprisingly complex structure, and now anyone could check that on their own computer.

▶ Try it: Conway's Game of Life (guide, Japanese)

The 1980s: looking for lifelike behavior in cellular automata

In 1984 Langton presented a tiny self-replicating loop that needs only 8 states. It is far smaller than von Neumann's machine, and you can watch it copy itself and spread. In 1986 came "Langton's ant," a single agent following two simple rules. It wanders in a messy way for roughly ten thousand steps, then suddenly settles into building a regular "highway." In the late 1980s Brian Silverman devised Wireworld, in which signals travel along wires. Draw a circuit and it behaves like an electronic one.

▶ Try it: Langton's Loops · Langton's Ant · Wireworld

1986: biomorphs and Boids

1986 also gave us two models that look alive on screen. In The Blind Watchmaker, Richard Dawkins described "biomorphs," a program that evolves branching drawings. You simply keep choosing the shapes you like, and unexpected "creatures" appear. Craig Reynolds introduced Boids, where each bird follows three rules: stay close to neighbors, don't collide, and match their heading (the paper appeared in 1987). There is no leader and no master plan, yet the flock moves as if it were one animal. Boids went on to be used in film animation and became a starting point for later work on collective motion.

▶ Try it: Boids (guide, Japanese)

1987: the field is founded

In September 1987 Langton convened an international workshop at Los Alamos National Laboratory. Its formal title was the "Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems." Computer scientists, biologists, physicists and robotics researchers came together, and "Artificial Life" became the name of a research field. The proceedings were published in 1989 as the book Artificial Life.

What set the meeting apart was its stance. The aim was not just to use computers as a tool for biology, but to explore the nature of life itself with computers. The Game of Life, self-replicating automata, Boids and biomorphs were all placed in a single story about what makes something lifelike.

1990s: evolving virtual creatures

In the 1990s the center of gravity moved from "making things that move" to "making things that evolve on their own." European conferences began in the early 1990s, and the journal Artificial Life was launched, giving the field a more settled shape.

• Tierra (Thomas Ray, 1990–91): self-replicating programs ran inside a computer and mutated. Ecosystem-like evolution appeared on its own, including parasites that hijacked other programs' copying code.
• Avida (early 1990s onward): a descendant of Tierra's approach that allows evolution experiments to be controlled carefully. It is still used in research on digital evolution.
• Evolved virtual creatures (Karl Sims, 1994): bodies and brains were evolved together, producing creatures that swim, walk, follow light and more. It was a pioneering example of evolving morphology and control at the same time.
• The Vicsek model (1995): particles move at a fixed speed and align with the average heading of their neighbors, plus a little noise. Vicsek and colleagues showed, in the language of physics, that the group switches abruptly from disordered motion to moving together.

▶ Try it: Neuroevolution · Vicsek model · EvoGym-inspired body and controller evolution

2000s–2010s: self-organization everywhere

From the 2000s onward, researchers studied, more precisely and in more variety, models in which a small change of the rules changes the whole world.

• NEAT (2002): a method that evolves both the wiring and the weights of a neural network. It became a foundation for later neuroevolution research.
• Physarum models (2010): Jeff Jones reproduced how slime mold searches for food and builds efficient transport networks, using the motion of simple particles. See the Physarum simulator.
• SmoothLife (2011): the Game of Life extended from a grid of cells to a smooth, continuous space, and a forerunner of Lenia. See SmoothLife.
• The Primordial Particle System (2016): Schmickl and colleagues showed that particles that only turn according to how many neighbors they have can form cell-like structures, which can even divide and merge. See Primordial Particles.

2018 onward: continuous worlds, and learning

Two currents stand out in recent years: making the world smoother and more continuous, and connecting ALife with machine learning.

• Lenia (Bert Chan, 2018–2019): SmoothLife taken further, with space, time and state all continuous. Hundreds of lifelike forms have been found that glide, wobble and divide. See Lenia.
• Particle Lenia (early 2020s): Lenia's ideas moved from a grid to particles. Particles move toward lower energy and form cell-like blobs. See Particle Lenia.
• Neural cellular automata (2020): instead of writing the update rule by hand, a neural network learns it. A single cell grows into a picture or shape, and can even repair itself when damaged. See Neural Cellular Automata.
• Evolution Gym (2021): a benchmark for evolving the body and controller of soft robots together, carrying Sims's idea into the machine-learning era.

Looking at this stretch, you can see research shifting from "humans write the rules" to "rules are discovered by learning and search."

The timeline at a glance

Three themes that keep returning

1. Local rules, global patterns.
   The Game of Life, Boids, Vicsek, primordial particles: after 80 years, the central discovery of ALife is the same. Simple rules that only look at neighbors can produce complex behavior across the whole system.
2. From discrete to continuous.
   The field has moved from grids of two-state cells (the Game of Life) to smooth continuous worlds (SmoothLife, Lenia). Continuous worlds tend to produce smoother, more organic motion.
3. From rules we write to rules we find.
   Early models used rules that people designed. With Sims's evolution, NEAT and neural cellular automata, the rules and even the body shapes are increasingly found by evolution and learning.

A suggested path through the collection

Playing with the models in historical order lets you retrace the discoveries yourself. Here is one route:

1. Grey Walter’s tortoise: watch it find and approach a light, and head back to its hutch when the battery runs low (1948).
2. Game of Life: watch a glider travel.
3. Wireworld: watch signals run through a circuit.
4. Langton's Ant: wait for what happens around step 10,000.
5. Boids: change the weight of each rule and watch the flock reshape.
6. Gray-Scott: nudge the parameters and see stripes turn into spots.
7. Lenia: watch smooth, creature-like forms move.
8. Neural Cellular Automata: see a learned rule grow a shape.

The simulator pages and the "how to use / theory" guides are bilingual (Japanese and English).

References

• Langton, C. G. (ed.) (1989). Artificial Life. Addison-Wesley (Santa Fe Institute Studies in the Sciences of Complexity).
• Langton, C. G. (1984). Self-reproduction in cellular automata. Physica D, 10, 135–144.
• von Neumann, J. (1966). Theory of Self-Reproducing Automata (edited and completed by A. W. Burks). University of Illinois Press.
• Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society of London B, 237, 37–72.
• Gardner, M. (1970). Mathematical Games: The fantastic combinations of John Conway's new solitaire game "life". Scientific American, 223(4), 120–123.
• Dawkins, R. (1986). The Blind Watchmaker. Longman.
• Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH '87.
• Ray, T. S. (1991). An approach to the synthesis of life. In Artificial Life II. Addison-Wesley.
• Sims, K. (1994). Evolving virtual creatures. SIGGRAPH '94.
• Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., & Shochet, O. (1995). Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 75, 1226–1229.
• Pearson, J. E. (1993). Complex patterns in a simple system. Science, 261, 189–192.
• Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.
• Jones, J. (2010). Characteristics of pattern formation and evolution in approximations of Physarum transport networks. Artificial Life, 16(2), 127–153.
• Schmickl, T., et al. (2016). How a life-like system emerges from a simple particle motion law. Scientific Reports, 6, 37969.
• Chan, B. W.-C. (2019). Lenia: Biology of artificial life. Complex Systems, 28(3), 251–286.
• Mordvintsev, A., Randazzo, E., Niklasson, E., & Levin, M. (2020). Growing neural cellular automata. Distill.
• Bhatia, J., et al. (2021). Evolution Gym: A large-scale benchmark for evolving soft robots. NeurIPS 2021.
• Levy, S. (1992). Artificial Life: The Quest for a New Creation. Pantheon.

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