I first heard about Huegene after watching the following video from Dave Ackley.

Similarities to Wa-Tor

Huegene is similar to Wa-Tor with the following differences;

1. Plants and herbivores rather than fish and sharks.

2. Plants cannot move like fish can. They spread through self propagation.

3. Herbivores have a probability chance of eating plants based on how close in color the herbivores and plants are.

Color Genes

Both the plants and herbivores have genes that determine their color.

When a new plant or herbivore is created it has a slightly mutated color from the parent. This results in plants growing in clumps of similar colors.

Plant Consumption Probability

This is the main difference between Huegene and Wa-Tor. In Wa-Tor the sharks happily gobble up any fish they land on. In Huegene the herbivores have a probability of eating a plant based on how close in color they are to the plant. So a blue herbivore will have a greater chance of eating a light blue plant than a yellow plant.

Herbivores eat the plants around them with the most similar color, which leads to the less similar plants in the area being able to thrive which then have more resistance to the herbivores in the area. The herbivores also mutate and can adapt to eating the new plant colors. This feedback cycle continues as the plants and herbivores adapt to the changes in each other.

Gene Color Methods

For the gene colors I use the following 3 options;

1. Hue. Hue is a floating point value between 0 and 1. For display the HSL(hue,1.0,0.5) value is converted to RGB.

2. RGB. Each plant or herbivore has 3 color genes for red, green and blue.

3. Hue and Saturation. Hue is between 0 and 1. Saturation is between 0.5 and 1. For display the HSL(hue,saturation,1) value is converted to RGB.

The following three sections explain how these different gene methods are used to determine a probability of a herbivore eating a plant next to it.

Hue Genes

Hue is a floating point value between 0 and 1. It is converted to RGB values for display.

The following code is used to determine if a herbivore eats a plant near it.

//difference between hue values
//wraparound difference - make sure difference is between 0 and 0.5
if difference>0.5 then difference:=1-difference;
//scale difference to between 0 and 1
//test probability
if random*probabilityfactor<(1.0-difference) then EatPlant

The difference takes into account the fact that the Hue values wrap around from 1 back to 0 on the hue color wheel. For example, if you have a plant hue at 0.1 and a herbivore hue at 0.9 you want their difference to be calculated as 0.2 and not 0.8.

I also added a “probability factor” in. If this is greater than 1 it lessens the chance of the herbivore eating the plant. If you get a simulation that the herbivores are too plentiful increasing this factor will help keep them under control.

RGB Genes

Each plant and herbivore now has 3 red green and blue values that are used for display and the probability of the plants being eaten by herbivores.

//difference between RGB values
//test probability
if random*probabilityfactor<(1.0-difference) then EatPlant

Hue and Saturation

Now takes into account hue and saturation values. Hue is between 0 and 1. Saturation is between 0.5 and 1.

//difference between hue values
//wraparound difference - make sure difference is between 0 and 0.5
if difference>0.5 then difference:=1-difference;
//saturation contributes 50% of difference value
//test probability
if random*probabilityfactor<(1.0-difference) then EatPlant

Other Examples

Plants actively look for empty neighbor locations to spread into. Herbivores move at random rather than actively looking for plant neighbors.

Plants actively looking for empty neighbors. Herbivores hunting for available plant neighbors.

Plants spread randomly. Herbivores hunt for available plant neighbors.

Sample Movie

Extension Into Three Dimensions

This next movie extends 2D into 3D. Same logic and parameters as the 2D movie, just adding the third Z dimension into the calculations.

The next movie averages the colors of the cells for display. The simulation genes themselves are not averaged/blurred, just the display colors. This cuts down the color noise and allows the more general waves of plant types to be seen. Or maybe it doesn’t? Anyway, it is another option to experiment with.


Huegene is now included in Visions of Chaos.



Wa-Tor is a simulation of fish vs sharks in a toroidal wraparound world. It was devised by AK Dewdney in 1984 and published in Scientific American under the name “Sharks and fish wage an ecological war on the toroidal planet Wa-Tor

The original Wa-Tor simulations took place on a VAX system with a display 80×23 characters in size. Each step of the simulation took a while to run (no exact time specs given). It is interesting to read in Dewdney’s article that to test a theory he had about the system he let a setup run overnight to see what happened in the morning. Now with a decent computer you can run these over thousands of cells in almost real time.


The world consists of a 2D array that wraps around at the edges. When you have the wrapped edges the world becomes a donut (or toroid) shape.

A number of sharks and fish are randomly distributed into the world.

Fish look for empty cells directly next to themselves (using a von Neumann neighborhood of neighbor cells directly north, south, east and west) and randomly move into one of them. If they are older than a specified breed age they will leave a child fish behind in their current location.

Sharks look for fish next to themselves and randomly eat one of them if found. When they move they will leave a child shark in their old spot if they are older than a specified breed age. If no fish are found they will randomly move into an available empty space. If they do not eat their starvation level increases. If they starve for too many steps of the simulation they die and are removed from the world.

Here is an example movie Wa-Tor. Green pixels are fish, red pixels are feeding sharks, gray pixels are sharks.

If you balance the settings right this sort of simulation will continue to cycle indefinitely.

Wa-Tor Parameters

Wa-Tor is controlled by the following parameters the user can play with;

Fish breed age – how many simulation steps does a fish need to survive for before it can give birth to baby fish. For these simulations I used 3 cycles.

Shark breed age – how many simulation steps does a shark need to survive for before it can give birth to baby fish. 15 cycles.

Shark starve time – how many simulation steps it takes for a shark to starve to death if it does not eat. 10 cycles.

Random Movement

An alternative method is to allow fish and sharks to only move randomly.

Fish pick one of the 4 directions at random. If it is empty they move into it and potentially have a child. If it is occupied they do nothing.

Sharks also pick a random direction. If a fish is there the shark eats it. If the spot it empty the shark moves into it. If it is occupied by another shark the shark does nothing.

Here is an animated gif showing fish and sharks both moving randomly.

Color Fish

This was a quick idea I had. Each fish has a color assigned to it. When it has a child fish, the child fish has the same color with slightly different RGB values. This way you can see how a “family” of similar fish spreads.


Wa-Tor can also be extended to 3D. In the following movie the simulation takes place in a 3D cube of cells, but cells outside a centered sphere are hidden to see the inner structures more clearly.

In this next movie only the fish are visible and they are colored to show how fish and their offspring spread.


Wa-Tor is available in Visions of Chaos.


Combinations Cellular Automata

Combinations Cellular Automaton

This is a new idea for a 1D cellular automata that came from Asher (blog YouTube).

Combinations Cellular Automaton

Trying to explain this clearly is harder work than programming it.

Combinations Cellular Automaton

Rule string

Combinations CAs can have between 2 and 10 maximum states per rule. 2 states means the cells can be either on or off. 3 states mean cells have 1 dead state and 2 living states.

The CAs are governed by a rule string.

The rule string needs to be states^2 characters in length. For 2 state rules the rule string will need 4 characters, for 4 states the rule string needs 16 characters, for 10 states the rule string needs 100 characters.

For 2 states the rule string characters are in base 2, so binary values of either 1 or 0. For 3 states the rule string is base 3, so the characters are all between 0 and 2. For 10 states the rule string is base 10, so the characters are all between 0 and 9.

An example 2 state rule is 0110 which creates the following;

An example 3 state rule could be 012120200 that creates the following result;

An example 10 state rule could be


which gives this result;

Number of possible rules

When you have a maximum of 2 states, the rule string has 4 digits of either 0 or 1. The maximum rule string is 1111, which converts to 15 in binary. That gives you 16 possible rules (0000 is also counted) for state 2.

Increasing to 3 states has a 9 digit rule string with base 3 digits. 222222222 is the maximum base 3 number which translates to 19682, so there are 19683 possible 3 state CA rules.

4 states is 16 characters. 3333333333333333 is 4,294,967,295 in decimal, so 4,294,967,296 possible 4 state rules.

10 states is 100 characters. No need for decimal conversion as base 10 is decimal. So 1 with 100 zeros after it possible rules. That may as well be an infinite space to look for rules within.

Combinations Cellular Automaton

How the cells are updated

Each cell in the CA is updated by taking into account 2 cells from the previous step. These 2 cells are converted into a decimal value that is used as an index in a rule string to give the new cell state. I bet that makes loads of sense! Let me try and explain.

An example with a 2 state CA using rule 0110, so cells can only be either state 0 or state 1.

If the first step is a single center cell, then the values would look like;


Cell at ? looks at the cell above it and the cell next to it. These are 00. 00 converted to decimal is 0. So we look at the 0th entry in the rule string, so the cell value becomes 0.


Same for the next 3 cells


Then we get to the next ? cell


This cell has the cell above it and next to it as 01. 01 converts to decimal 1, so we look at the 1th (second) digit in the rule string. The second digit in the rule is a 1, so the new cell becomes a 1.


The next ? cell now has 10 above it. This converts to a 2 in decimal, so we look at the third rule digit. Again it is a 1.


The rest of the row has 00 above them, so they get set to 0.


If you continue this process, you get the following result;

Brick wall neighborhood

This CA also uses the brick wall neighborhood idea for selecting neighborhoods. See here for an animation of brick wall. Basically you shift the neighbor locations being checked every other line.

For the Combinations Cellular Automata in this post this means the location of the 2 neighbor cells changes every line.

For even lines you use the cell directly above and to the right. For example, in this next diagram the ? cell would use the 3 and 4 locations.


For odd lines you use the cell to the left and the cell above. This would be the 2 and 3 neighbor cells.

Combinations Cellular Automaton

Interesting Rules

Rule 002200121 behaves similar to Wolfram’s Rule 30. During bulk runs of rules I have seen other results that also look like this one.

Combinations Cellular Automaton

Another result that has been observed to occur are these “counting triangles” rules. These show similar behavior to Wolfram’s “Rule 225“. The splitting of bifurcation like structure on the right hand side is like a binary counter. This is rule 010221200.

Combinations Cellular Automaton

Rule 200122011 also gives a binary counter structure.

Combinations Cellular Automaton

More example images

See my flickr gallery for more example results.

Combinations Cellular Automaton


Combinations Cellular Automata are now included in Visions of Chaos.


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