Alternating Neighborhoods Cyclic Cellular Automata


What happens when you combine the Alternating Neighborhoods with Cyclic Cellular Automata? This was an idea emailed to me from Asher (check out his blog and YouTube).

Range 1 Neighborhoods

Firstly for CCAs that only use neighbor cells within a range of a single cell. Rather than check all 8 neighbor cells each step of the CA, the neighborhood cells alternate between the following neighborhoods each cycle. The green square is the current cell being processed and the red squares are the neighborhood cells counted.

Range 1 Results

Expanding Stripes – Range 1 – Threshold 1 – States 3

Large Spirals – Range 1 – Threshold 1 – States 15

Spirals – Range 1 – Threshold 2 – States 4

Range 2 Neighborhoods

And then I also extended the neighborhoods to Range 2 and I give the option to use either of the following layouts.

The second neighborhoods tend to give more squarish shaped spirals compared to the first neighborhoods.

The usual CCA rules apply, but the neighborhoods above are used to calculate the count of values that is checked against the threshold.

Range 2 Results

Dithered Spirals – Range 2 – Threshold 2 – States 5

Small Spirals Form In Larger Spirals – Range 2 – Threshold 2 – States 4

Stable Blobs – Range 2 – Threshold 7 – States 2


Alternating Neighborhoods Cyclic Cellular Automata are now included in Visions of Chaos.


3D Cellular Automata

This is a post to provide info and answer some questions raised in the comments of the following YouTube movie.

Cellular Automata in 3D

3D Cellular Automata are extensions of the more common 1D Cellular Automata and 2D Cellular Automata into the third dimension. Rather than just checking neighbor cells in the X and Y directions, the Z direction is also included.


Neighborhoods in CA refers to which cells around each cell influence it’s birth, survival and death.

The two most common types of cell neighborhoods used in 2D CA are Moore and Von Neumann.

For 3D Moore extends to 26 possible neighbors (think of a Rubik’s cube with the middle of the cube as the current cell). Or consider a 3x3x3 3D grid of little cubes. The interior cube is the current cell, so the remaining 26 cubes around it are the neighbors of the center cube.

3D Von Neumann uses only neighbor cells sharing a face with current cell. This gives the 6 cells in the +/- X, Y and Z axis direction from each cell. Think of a 3D “plus sign” or cross shape.

Rules Explained

Rule 445 is the first rule in the video and shown as 4/4/5/M. This is fairly standard survival/birth CA syntax.
The first 4 indicates that a cell survives if it has 4 neighbor cells.
The second 4 indicates that a cell is born in an empty location if it has 4 neighbors.
The 5 means each cell has 5 total states it can be in (state 4 for newly born which then fades to state 1 and then state 0 for no cell)
M means a Moore neighborhood.

Another rule is Clouds 1 shown as 13-26/13-14,17-19/2/M
Alive cells with 13,14,15,16,17,18,19,20,21,22,23,24,25 or 26 neighbors survive.
Empty cells with 13,14,17,18 or 19 neighbors have a new cell born at that location.
2 states. Cells are either dead or alive. No refractory period they fade from birth to death.
M means a Moore neighborhood.

More than 2 states can be confusing at first. In a 2 state CA when a cell dies it goes immediately from living (state 1) to dead (state 0). In more than 2 states, when a cell dies it does not immediately go to state 0. Instead it fades out to state 0. If there are 5 total states then a live cell with state 4 (4 not 5 as the possible state values are 0,1,2,3 and 4) fades to state 3, then 2, then 1 and finally disappears at state 0.

Answering Questions and Responding to Comments

Some people refer to the 3D CA as the “Game of Life” or “Brian’s Brain”. This is wrong. “Game of Life” is a specific rule of 2D CAs and it does not have a direct equivalent in 3D. The movie above is a 3D Cellular Automaton, not a “3D Game of Life”. When referring to these CAs call them 3D Cellular Automata, not 3D Life or 3D Brain or whatever else.

The music was a quick composition by me using FL Studio.

I am glad most people seem to like the movie. I have to give a shout out to the Mitsuba Renderer. Mitsuba is responsible for rendering the very nicely shaded little cubes that allow the structures of the CA rules to be seen so clearly.

If you have any other 3D CA questions, leave a comment here or in the YouTube video comments and I will try and address them here in the future. Cellular Automata are a fairly simple concept once you understand the basics of how they work.


Automatic Detection of Interesting Cellular Automata

This post has been in a draft state for at least a couple of years now. I revisit it whenever I get inspiration for a new idea. I wasn’t going to bother posting it until I had a better solution to the problem, but maybe these ideas can trigger a working solution in someone else’s mind.

Compared to my other blog posts this one is more rambling as it follows the paths I have gone down so far when trying to solve this problem.


Cellular automata tend to have huge parameter search spaces to find interesting results within. The vast majority of rules within this space will be junk rules with only a small fraction of a percentage being interesting rules. I have spent way too many hours repeatedly trying random rules when looking for new interesting cellular automata. Between the boring rules that either die out or rules that go chaotic there is that sweet spot of interesting rules. Finding these interesting rules is the problem.

Needle in a haystack

My ideal goal has always been to be able to run random rules repeatedly hands free and have software that is “clever” enough to determine the difference of interesting vs boring results. If the algorithms are good enough at detecting interesting then you can come back to the computer hours or days later and have a set of rules in a folder with preview images and/or movies to check out.

I want the smarts to be smart enough to work with a variety of CA types beyond the basic 2 state 2D cellular automata. Visions of Chaos contains many varieties of cellular automata with varying maximum cell states, dimensions and neighborhoods that I ultimately would like to be able to click a “Look for interesting rules” button.

Interesting Defined

Interesting is a very loose term. Maybe a few examples will help define what I mean when I say interesting.

Boring results are when a CA stabilizes to a fixed pattern or a pattern with very minimal change between steps.

Cellular Automaton

Cellular Automaton

Cellular Automaton

Chaotic results are when the CA turns into a screen of static with no real discernible patterns or features like gliders or other CA related structures. For a CA classifier these rules are also boring.

Cellular Automaton

Cellular Automaton

Cellular Automaton

Interesting is anything else. Rules like Game of Life, Brian’s Brain and others that create evolvable structures that survive after multiple cycles of the CA. This is what I want the software to be able to detect.

Cellular Automaton

Conway’s Game of Life – 23/3/2

Cellular Automaton

Brian’s Brain – /2/3

Cellular Automaton

Fireballs – 346/2/4

My Previous Search Methods

1. Random rules. Repeatedly generate random rules hoping to see an interesting result. Tedious to say the least, although the majority of the interesting cellular automata rules I have found over the years have been through repeatedly trying different random rules. While a boring TV show or movie is on I can repeatedly hit F3, F4 and Enter in Visions of Chaos while looking for interesting results. F3 stops the current CA running, F4 shows the settings dialog, Enter clicks the Random Rule button.

2. Brute force all possible rules. Only applicable for when the total number of rules is small (possible for some of the simpler 1D CAs). Most 2D CAs have millions or billions of possible rules and brute force rendering them all and then checking manually is impossible.

3. Mutating existing interesting rules. If you get an interesting rule, you can try mutating the rule slightly to try alternatives that may behave similarly yet better to the rule. Slightly usually means toggling one of the survival/birth checkboxes on/off. This has occasionally helped me find interesting rules or refine a rule to that sweet spot. The problem with CAs is that even changing one checkbox will usually result in a completely different result. The good results do not tend to “clump” together in the parameter space.

The rest of this blog post contains methods others and myself have tried to classify cellular automata behavior.

Wolfram Classification

Stephen Wolfram

Stephen Wolfram defined a rough set of 4 classifications for CAs.

Class 1: Nearly all initial patterns evolve quickly into a stable, homogeneous state. Any randomness in the initial pattern disappears.

Class 2: Nearly all initial patterns evolve quickly into stable or oscillating structures. Some of the randomness in the initial pattern may filter out, but some remains. Local changes to the initial pattern tend to remain local.

Class 3: Nearly all initial patterns evolve in a pseudo-random or chaotic manner. Any stable structures that appear are quickly destroyed by the surrounding noise. Local changes to the initial pattern tend to spread indefinitely.

Class 4: Nearly all initial patterns evolve into structures that interact in complex and interesting ways, with the formation of local structures that are able to survive for long periods of time.

Classes 1 to 3 would be considered “boring” for anyone trying random rules. Class 4 is that “sweet spot” of CAs that something interesting happens between dying out and chaotic explosions.

You can look at a CA after it has been discovered and put it into one of those 4 categories but that doesn’t help detecting interesting rules in Class 4.

Other Methods From Various Papers

Here are some other classification methods in papers I found or saw mentioned elsewhere. The mathematics is beyond me for most of them. I wish papers included a small snippet of source code with them that shows the math. I always find it much easier understanding and implementing some source code rather than try and understand formal equations.

Behavioral Metrics

Search Of Complex Binary Cellular Automata_Using_Behavioral_Metrics.


Wolfram’s Universality And Complexity In Cellular Automata discusses “entropy” values that I don’t understand.

Lyapunov Exponents

Stability Of Cellular Automata Trajectories Revisited : Branching Walks And Lyapunov Profiles.

Towards The Full Lyapunov Sprectrum Of Elementary Cellular Automata.

Kolmogorov–Chaitin Complexity

Asymptotic Behaviour And Ratios Of Complexity In Cellular Automata.

Genetic Algorithms

Searching For Complex CA Rules With GAs.

Evolving Continuous Cellular Automata For Aesthetic Objectives.

Extracting Cellular Automaton Rules Directly From Experimental Data.

Other Papers

Pattern Generation Using Likelihood Inference For Cellular Automata. 1D CAs.


Jeff Heaton uses genetic mutations to evolve cellular automata.

Langton’s Lambda

Chris Langton

Chris Langton defined a single number that can help predict if a CA will fall within the ordered realm. See his paper Computation at the edge of chaos for the mathematical definitions etc.

Langton called this number lambda. According to this page Lambda is calculated by counting the number of cells that have just been “born” that step of the CA and dividing it by the total CA cells. This gives a value between 0 and 1.

L = newlyborn/totalcellcount
L within 0.01 and 0.15 means a good rule to further investigate.

So if the grid is 20×20 in size and there were 50 cells that were newly born that CA cycle, then lambda would be 50/20×20=0.125

I skip the first 100 CA cycles to allow the CA to settle down and then average the lambda value for the next 50 steps.

As stated here there is no single value of lambda that will always give an interesting result. Langton’s paper and example applet are only concerned with 1D CA examples. I really want to find methods to search and classify 2D, 3D (and even 4D) cellular automata.

Rampe’s Lambdas

For lack of a better name, these are the “Rampe’s Lambda” values I experimented with as alternatives to Langton’s Lambda.

R1 = newlyborn/newlydead
R1 within 0.9 and 1.1 means a good rule to further investigate.

R2 = abs(newlyborn-newlydead)/totalcellcount
R2 within 0.001 and 0.005 means a good rule to further investigate.

R3 = (newlyborn+newlydead)/totalcellcount
R3 within 0.01 and 0.8 means a good rule to further investigate.

R4 = ((newlyborn/totalcellcount)+(newlydead/totalcellcount))/2
R4 within 0.01 and 0.23 means a good rule to further investigate.

R5 = % change in Langton’s Lambda between the last and current CA cycle
R5 within 0.01 and 0.1 means a good rule to further investigate.

Again, skip the first 100 cycles of the CA and then use the average lambda from the following 50 cycles.

Lambda Results

All of them (both Langton and my “Rampe” variations) are next to useless from my tests. I ran a bunch of known good rules and got mixed results. All the lambda’s gave enough false positives to not be of any use in searching for interesting new rules. You may as well use a random number generator to classify the rules.

Maybe they can be used to weed out the extreme class 1, 2 and 3 uninteresting dead rules, but they are not useful for classifying if a class 4 like result is interesting or not.

Fractal Dimension

Fractal Dimension CA Search

Another method I tried is finding the fractal dimension of the CA image using box counting. Fractal dimensions are unlike the usual 1D, 2D and 3D fixed dimensions and for a 2D image are and floating point value between 0 and 2.

The above screenshot shows the fractal dimension tests on existing sample interesting CA files. The results are all over the place with no “sweet spot” of dimension correlating to interesting. The way it works is that each CA is run for 50 steps, the image is converted to black and white (non black pixels in the image are changed to white) and then the dimension is calculated using the box counting method.

Increasing the range of dimension for “good” detection may result in the known interesting rules to pass the tests, but it then thinks a lot of uninteresting rules are then interesting, meaning you still need to manually sort good vs bad.

A fractal dimension between 1.0 and 1.4-1.5 can help weed out obvious “bad” results, but is really not helpful in hands free searching.

Compression Based Searching

Another new interesting idea on CA searching comes from Hugo Cisneros, Josef Sivic and Tomas Mikolov. Using data compression algorithms to rate CAs.

Their paper “Evolving Structures in Complex Systems” available here is an interesting read.

Source code accompanying the paper is provided here.

Neural Networks – Part 1

This was an idea I had for a while. Train a neural network to detect if a CA rule is interesting or not.

I was able to implement a rudimentary neural network system after watching these excellent videos from Dan Shiffman.

I went from almost zero knowledge of the internals of neural networks to much more comfortable and being able to code a working NN system. If you want to learn about the basics of coding a neural network I highly recommend Dan’s playlist.

For a neural network to be able to give you meaningful output (in this case if a CA rule is interesting or not) it needs to be trained with known good and bad data.

I tried creating a neural network with 19 inputs (9 for survival states, 9 for birth states and 1 for number of states) to cover the possible CA settings, ie

2D CA Rules

The neural network has 19 inputs, a number of neurons in the hidden layer and a single output neuron that does the interestingness prediction.

Neural Network

I mainly kept the hidden neuron count the same as the inputs, but I did experiment with other counts as the next diagram shows.

Neural Network

The known good and bad rules are fed through the neural network in random order for 10 million or more times. You can see how well the network is “learning” by tracking the mean squared error. As you repeatedly feed the network known data the error value should drop meaning the network is becoming more accurate at predicting the results you train it with.

Once the network is trained, you can run random rules and see if the prediction of the network matches your rating of if the CA is interesting or not. You can also repeatedly try random rules until they pass a threshold level of interesting. Every time a prediction is made the human can rate if the detection was correct. These human ratings are added back to the good and bad rule training pool so they can be used the next time the network is trained.

The end result is “just OK”. I used a well trained network (with a mean squared error of around 0.001) and got it to repeatedly try random rules until it found a rule it predicted would be interesting. The results are not always interesting. More interesting than purely sitting there clicking random repeatedly as I have done in the past, but there are still a lot of not interesting rules spat out. If I let the network run for a few hours and got it to save every rule it predicted to be interesting I would still have a tedious process of weeding out the actual interesting rules.

I don’t think inputs from survival and birth rules is the best way of doing this. This is because a toggle of a single survival or birth checkbox will usually drastically change the results from interesting to boring or just chaos. Also changing the maximum states each cell can have by 1 will cause well behaved rules to change into chaotic mess results.

One idea I need to try is using a basic NN like this that uses the lambda values above for inputs. Maybe then it can work out which combination of lambdas (and maybe fractal dimension) work together to create good rules. This is worth experimenting with when I get some time.

Neural Networks – Part 2

This time I am trying to get the network to detect interesting CAs by using images from a frame of the CAs. For each of the known good and bad rules I take the 100th frame as an input. I also repeat each of the rules 100 times to get 100 samples of each rule.

If I use a 64×64 sized grayscale image then there will now be 4,096 inputs to the network. Add another 100 hidden nodes and that makes a large and much slower network when training.

Run the CA rules on a 64×64 sized grid, convert the image pixels into the 4,096 inputs and train the network.

So far, no good results. The mean squared error falls very slowly. Maybe it would get better after days of training, but I am not that patient yet.

This online example and this article show how this method (a fully connected neural network) is never as accurate as a convolutional neural network. So, onto Part 3…

Neural Networks – Part 3

My next idea was to try using Convolutional Neural Networks. See here for a nice explanation of convolutional neural networks.

Convolutional Neural Network

CNN’s are made for image processing, feature extraction and detection. If a CNN can be trained to recognize digits and tell if a photo is of a cat or a dog then I should be able to use a CNN to “look at” a frame of a cellular automaton and tell me if it is interesting or not.

After watching a bunch of YouTube university lectures and tutorials on CNNs I decided not to extend my existing neural network code to handle CNNs. For the network sizes I will be training I need a real world library. I chose Google’s TensorFlow.

TensorFlow Logo

TensorFlow supports GPU acceleration with CUDA and is magnitudes faster and more reliable than anything I could code.

Once I managed to get Python, TensorFlow, Keras, CUDA and cuDNN installed correctly I was able to execute Python scripts from within Visions of Chaos and successfully run the example TensorFlow CNN MNIST code. That showed I had all the various components working as expected.

Creating Training Data for the CNN

Acquiring clean and accurate training data is vital for a good model. The more data the better.

I used the following steps to create a lot of training images;

1. Take a bunch of CA rules that I had previously ranked as either good or bad.

2. Run all of them over a 128×128 sized grid for 100 steps and save the 100th frame as a grayscale jpg file.

3. Step 2 can be repeated multiple times to increase the amount of training data. CAs starting from a random grid will always give you a unique 100th frame so this is an easy way to generate lots more training data.

4. Copy some of the generated images into a test folder. I usually move 1/10th of the total generated images into a test folder. These will be used to evaluate how accurate the model is at predictions once it has been trained. You want test data that is different to the data used to train and validate the model.

Good Cellular Automata Training Data

Examples of good CA frames

Bad Cellular Automata Training Data

Examples of bad CA frames

Quantity and Dimensions of Training Data Images

I tried image sizes between 32×32 pixels and 128×128 pixels. I also tried various zoomed in CA images with each cell being 2×2 pixels rather than a single pixel per cell.

For image counts I tried between 10,000 up to 300,000.

After days of generating images and training and testing models I found a good balance between image size and model accuracy was images 128×128 pixels in size with a single pixel per CA cell (so a CA grid of 128×128 too).

I also experimented with blurring the images thinking that may help search for more general patterns, but it did not seem to make any difference in the number found or accuracy of results.

One thing working with neural networks teaches you is patience. Generating the images is the slowest part of these experiments. If anyone is willing to gift me some decent high end CPUs and GPUs I would put them to good use.

Custom Input for CNNs

The best videos I found on using CNNs with custom images were these videos on YouTube by sentdex. Parts 1 to 6 of that playlist got me up and running.

Creating the Training Data for TensorFlow

Once you have your training images they need to be converted into a data format that TensorFlow can be trained with.

Again, I recommend the following sentdex video that covers how to create the training data.

The process to convert the training images into training data is fast and should not take longer than a minute or two.

Model Variations

Time to actually use this training data to train a convolutional neural network (what TensorFlow calls a model).

There are a wide variety of model and layer types to experiment with. For CNNs you basically start with one or more Conv2D layers followed by one or more Dense layers and finally a single output node to predict a probability of the image being good or bad.

Here are some models I tried during testing. From various sources and videos and pages I have seen. Running on an Nvidia 1080 GPU took around 2 hours per model to train (50 epochs each with 100,000 training images), which seemed lightning fast after waiting 30 hours for my training images to generate.

# Version 1
# Original model from sentdex videos

model = Sequential()

model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))

model.add(Conv2D(64, (3,3)))




model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"]), y, batch_size=50, epochs=50, validation_split=0.3)

When the 50 epochs finish, you can plot the accuracy and loss vs the validation accuracy and loss.

TensorFlow training graph

Version 1 gave these results;
test loss, test acc: [0.13674676911142003, 0.9789666691422463]
98% accuracy with a loss of 13%
When I test a different unique set of images as test data I get;
14500 good images predicted as good – 301 good images predicted as bad – 97.97% predicted correctly
14653 bad images predicted as bad – 184 bad images predicted as good – 98.76% predicted correctly

One thing the above “Model loss” graph shows is overfitting. The val_loss graph should follow the loss graph and continue to go down. Instead of going down the line starts going up around the 5th epoch. This is an obvious sign of overfitting. Overfitting is bad. We don’t want overfitting. See here for more info on overfitting and how to avoid it.

The second suggestion from here mentions dropouts. Dropouts remove random links between nodes in the model network as it trains. This can help reduce overfitting. So let’s give that a go.

# Version 2
# Original model from sentdex videos
# Adding dropouts to stop overfitting

model = Sequential()

model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))

model.add(Conv2D(64, (3,3)))




model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"]), y, batch_size=50, epochs=50, validation_split=0.3)

50 epochs finished with this graph.

TensorFlow training graph

Now the validation loss continues to generally go down with the loss graph. This shows overfitting is no longer occurring.

Version 2 gave these results;
test loss, test acc: [0.037338864829847204, 0.9866000044345856]
98% accuracy with a 13% loss
When I test a different unique set of images as test data I get;
14151 good images predicted as good – 68 good images predicted as bad – 99.52% predicted correctly
14326 bad images predicted as bad – 12 bad images predicted as good – 99.92% predicted correctly

# Version 3

model = Sequential()

model.add(Conv2D(32, (3,3), input_shape = X.shape[1:]))

model.add(Conv2D(64, (3,3)))

model.add(Conv2D(128, (3,3)))

model.add(Conv2D(128, (3,3)))




model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"]), y, batch_size=50, epochs=50, validation_split=0.3)

Graphs looked good without any obvious overfitting.

Version 3 gave these results;
test loss, test acc: [0.03628389219510306, 0.9891333370407422]
98% accuracy with 3% loss. Getting better.
When I test a different unique set of images as test data I get;
14669 good images predicted as good – 59 good images predicted as bad – 99.60% predicted correctly
14490 bad images predicted as bad – 62 bad images predicted as good – 99.57% predicted correctly

# Version 4

model = Sequential()

model.add(Conv2D(32, (3,3), input_shape = X.shape[1:]))
model.add(Conv2D(32, (3,3)))
# BatchNormalization better than Dropout?
# model.add(BatchNormalization())

model.add(Conv2D(64, (3,3)))
model.add(Conv2D(64, (3,3)))
# BatchNormalization better than Dropout?
# model.add(BatchNormalization())

model.add(Conv2D(128, (3,3)))
model.add(Conv2D(128, (3,3)))
# BatchNormalization better than Dropout?
# model.add(BatchNormalization())






model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"]), y, batch_size=50, epochs=50, validation_split=0.3)

For this model I threw in multiple ideas from all previous models and more.

Version 4 gave these results;
test loss, test acc: [0.031484683321298994, 0.9896000043551128]
99% accuracy with a 3% loss. Best result so far.
When I test a different unique set of images as test data I get;
14383 good images predicted as good – 119 good images predicted as bad – 99.18% predicted correctly
14845 bad images predicted as bad – 4 bad images predicted as good – 99.97% predicted correctly

For the rest of my tests I used Version 4 for all training.

Tweaking your CNN models

See the sentdex videos above for a good example of how to tweak models and see how the variations rate. Use TensorBoard to see how they compare and optimize them.

TensorBoard Graphs

TensorBoard has other interesting histograms it will generate from your training like the following. I have no idea what this is telling me yet, but they look cool. Using histograms did seem to slow down the training with extended pauses between epochs, so unless you need them I recommend disabling them.

TensorBoard Histograms

Testing the Trained Model

Now it is finally time to put the model to the test.

Randomly set a CA rule, run it for 100 generations and then use model.predict on the 100th frame. This takes around 6 seconds per random rule.

The model.predict function returns a floating point value between 0 and 1.
Between 0 and 0.2 are classified as bad.
0.2 to 0.95 are classified as unsure.
0.95 to 1 are classified as good.

The prediction accuracy is better than any of the other methods shown previously in this post.

The rules it did detect in the bad category were all bad, so it does a great job there. No interesting rules got incorrectly classified as bad from my tests. I can safely ignore rules classified as bad which speeds up the search time as I don’t have to re-run the rules and create a sample movie.

The detected good rules did have a blend of interesting and boring/chaotic, but there were a lot less of them to check. Roughly 1% of total random rules are classified as good. The rules the model incorrectly predicts as interesting can be moved into the “known bad” folder and can be added to the next trained model (another 40 hours or so of my PC churning away generating images and training a new model).

The rules it predicted in the unsure 0.2 to 0.95 range did have features that were in the range between good and bad. Some of them would have made excellent good samples if only they were not as chaotic and “busy”.


Here are some examples found from overnight convolutional neural network searches.

Cellular Automaton

TF247764 – 4567/234568/3 – Brian’s Brain with islands

Cellular Automaton

TF394435 – 34/256/3 – Another Brian like rule

Cellular Automaton

TF459651 – 345/345/3 – Blobby balance between life and death

Cellular Automaton

TF174642 – 15678/12678/2 – Solid islands grow amongst static

Cellular Automaton

TF1965283 – 1235678/012567/3 – Amoebas living in chaos

Cellular Automaton

TF3392484 – 014567/34567/4 – Amoebas on a stable background

Other CNN Problems and Ideas

One problem is that CNNs seem to only detect shapes/gliders/patterns that are similar to the training data. After days of testing self searching with the CNN models there were no brand new different rules discovered. Just a bunch of very similar to existing rules and maybe a few slight tweaks. For example if a CNN is trained using only examples of Conway’s Game of Life CA then it is not going to predict Brian’s Brain is interesting if it randomly tries the rule for Brian’s Brain. The CNN needs to have previously seen the rule(s) it will detect as interesting. I did see slight variations found and scored as interesting, but for a new CA type without a lot of “good” rules to train on the CNN is not going to have problems finding new/different interesting rules. The main reason I want a “search for interesting” function is for when I have a new type of CA without a lot of known good rules. I want the search to be able to work without needing hundreds or thousands of examples of already rated good vs bad. Otherwise I need to sit there trying random rules for hours and manually rate them good or bad before training a new model specific to that CA rule.

Maybe using single frames is not the best idea. Maybe the difference between the 99th and 100th frame? Maybe a blur or average of 3 frames? This is still to be experimented with when I have another week to spend generating images and training and testing new models.

Then I thought maybe I am over training the models. If you train a neural network for too long it will overfit and then only be able to recognize the data you trained it with. This is as if it memorizes only the good data you gave it as good. It cannot generalize to detect other different good results as good. This results in new interesting CAs being potentially classified as bad. I did try lowering the training epochs from 50 to 10 to see if that helped detect more generalized interesting CA rules but it didn’t seem to make any difference. Even lowering it to 5 epochs trained a model that was still accurate at predictions. Plus the difference between random frames of good CAs shows it can detect gliders at different locations within frames.

Rather than train a model for each type of CA, train a model with examples from multiple CA types. Try and make the model more capable of general CA detection. Maybe it could then detect newer shapes/gliders in different new CA rules if it has a good general idea of what interesting CA features are from multiple different CAs. This may work? Another one for the to do list.

Convolutional Neural Networks (and neural networks in general) are not an instant win solution. You do need to do a lot of research about the various settings and do a lot of testing to get a good model which you can then use to predict the “things” you want the model to predict. But once you get a well trained model CNNs can be almost magical in how they can learn and be useful when solving problems.

The more I experiment with and learn about neural networks only makes me want to continue the journey. They really are fascinating. Using TensorFlow and Keras are a great way to get into the world of neural networks without having to code your own neural network system from scratch. I do recommend at least coding a basic feed forward neural network to get a good grip on the basics. When you jump into Keras the terminology will make more sense. YouTube has lots of good neural network related videos.

Availability to End Users

I have now included the trained (20 epochs Version 4 to hopefully leave a little room for finding more unique results) TensorFlow CNN model with Visions of Chaos. That means the end user does not need to do any image generation or training before using the CNN for searching. Python and TensorFlow need to be installed first, but after that the user can start a hands free search for interesting rules. When TensorFlow is installed and detected a search button appears on the 2D Cellular Automata dialog. Clicking Search starts a hands free random search and classification.

TensorFlow CNN CA Searching

The other search methods above are still hidden as they do not predict interesting with a high enough accuracy.

The End (for now)

If you managed to get this far, thanks for reading.

If you have some knowledge about any of the above methods that I missed please leave a reply or get in touch and let me know.

Any other ideas for cellular automata searching and classification are also welcome.

I will continue to update this post with any other methods I find in the future.


MergeLife Cellular Automata

A new variety of cellular automata from Jeff Heaton. His original paper describing MergeLife is here, but he also made the following video that clearly explains how the rules work.

Jeff’s MergLife page also has more info and examples you can run online.

MergeLife is now included with Visions of Chaos. I haven’t added the genetic mutations yet, but you can repeatedly click the random and mutate buttons and see what new patterns emerge.


Cellular Automata Explained – Part 1

My attempt at explaining cellular automata. Aimed at someone who has no real knowledge of how CAs work.

If you look at some online definitions of CAs you will see explanations like this and this. You can try and understand them, but probably be still left confused.

1D Cellular Automata

OK, let’s start at the simplest form of cellular automata, the one dimensional cellular automata.


A one dimensional CA consists of a line of row of cells. Each cell can be alive (on) or dead (off).

The first row of the image above is how this cellular automaton begins. It has 1 single centered alive (black) cell surrounded by dead (white) cells.

1D CA Rules

Cellular automata change states and evolve based on simple rules. The rules apply to every cell in the CA at the same time each step.

For the above image, the rules are as follows;


This shows the 8 possible rules for this CA. Each new cell state depends on itself and it’s 2 neighbors in the previous state.

The first rule (with the 3 black squares above a single white square) means that if the current cell and its two neighbors are black that cell will become a white cell the next step of the CA.

The second rule means that if a cell and its left neighbor are black but its right neighbor is white, the cell will be white in the next step.

The rest of the 6 rules are the same principal and cover all possible combinations of white and black cells.

You apply these rules to every cell in the CA each step. Here is a nice animation courtesy of Wikipedia showing how the rules are applied to a row of cells and the next step of the CA resulting from the rules.


Once the rules have been applied to all the cells, that step is completed. For 1D CA the easiest way to display them is to show each of the steps under the last in a 2D grid. The first 15 steps are shown in the grid above.

1D CA Rule Numbers

Looking again at the rule for this CA


you will notice that there are a series of 1’s and 0’s under the rules. 0 for if the rule creates a dead (white) cell and 1 if the rule creates an alive (black) cell. This series of 1’s and 0’s can be converted into a binary string that can then be converted into a number. For 8 digit binary numbers the numbers will range from 0 (for 00000000) to 255 (for 11111111).

These 1D CA’s have 256 possible combinations of rules. Rule 30 (shown above) is the conversion of 00011110 binary into digital 30. Being able to refer to each rule as a single number makes it easier to state which rule you are talking about.

More steps

If you follow the above rule repeatedly for more steps it evolves like this


From such simple rules some interesting structures arise within in. This specific rule has been used to generate random numbers (you can keep track of the center pixel going down the image and use it to create random numbers). You can see more info about Rule 30 here.

So what about the other 255 1D CA rules?

You can see the results of all 256 rules here.

An interesting result is rule 22.


Rule 22 results in a structure of triangles within triangles. This is a famous fractal pattern called the Sierpinski Triangle and it tends to pop up all over the place in cellular automata and fractals.

The End – for now

Hopefully by now you have a basic understanding of cellular automata. The main points to know are;

1. CAs are based on a grid of cells that are alive or dead.
2. The CA runs/updates/steps by running a set of rules on all the cells at the same time.
3. The same rules are then applied to the new cells and the process repeats itself.

The one dimensional cellular automata are kind of bland and once you seen the possible 256 rules there isn’t much excitement, but understanding 1D first makes the transition into higher dimensions and more complex rules easier to grasp.

If you want to experiment with these 1D CAs yourself, you can download Visions of Chaos. Open the 1D CA mode dialog and suddenly the options should be more clear to you now.


There is even an option that takes the CA steps and maps them into music. You can also print a catalog of all the 256 rules with more details.

If any of the above is not clear, please comment and let me know.


Extended Neighborhood 1D Cellular Automata

Extended Neighborhood Cellular Automaton

When I first saw this type of cellular automata described by Gugubo on Reddit I was sure I must have implemented it and included it in Visions of Chaos before, but a quick check showed it wasn’t a CA I had covered. There is enough info in the Reddit thread for me to code it up and put it into Visions of Chaos.

Extended Neighborhood Cellular Automaton

This is a 1D Cellular Automaton that uses 5 cells from the previous generation (2 either side and the central cell) to update the new cell state. The larger neighborhood with 2 cells either side is why I called these “Extended Neighborhood” in Visions of Chaos.

Extended Neighborhood Cellular Automaton

There are 4,294,967,296 (2^32) possible “rules” or types in this CA. Each of the rule numbers can be converted into a 32 digit binary number. For example, rule 260 becomes;

Extended Neighborhood Cellular Automaton

To update a cell, use the following steps;
1. Convert the previous step’s left 2 cells, current cell, and right 2 cells into a binary value.
i.e LLCRR may have states 11010 which can be converted into decimal 26
2. Counting from right to left on the binary representation of the rule above, the 26th digit is a 0, so the new cell state is a 0.

Extended Neighborhood Cellular Automaton

The process is repeated for all cells and then repeated for all rows as long as the CA runs.

Extended Neighborhood Cellular Automaton

I also added the option for more than 2 states (alive or dead) per cell. This way, when a cell dies it does not turn instantly into a dead cell, but has a delayed dying period. If there are 4 states per cell then a living cell (state 1) that dies will first go to state 2, then state 3, then finally die (state 0). Only newly born state 1 cells are used in the rule. All other non state 1 cells are considered state 0 when updating the cell based on the binary string rule.

Extended Neighborhood Cellular Automaton


Multiple Rules Cellular Automata

Another new CA to experiment with.

The idea for these came from a comment Tsui Kagura left on one of my YouTube videos.

Can you make a CA that is 4D ( or 5D etc ) in a sense, that you use 2 (or more) different regular CA rules, run them in the same space (same coordinates), same time (same steps), but then, based on some kind of rule also have them interact with each other?As if they were neighborhoods from another dimension. I assume we could only be ‘seeing’ one of them, the rest would be in a hidden dimension, but affecting the one we’re looking at. If there are more than one hidden dimensions, they could affect each other too, maybe using a different rule between them…

Using multiple rules on the same CA is something I have not experimented with before, so I had to give it a go.

To start off I used the simplest 2 state 2D CAs. The extension from a usual 2D CA is simple enough. You run 2 rules over the same grid. Store the results of each rule (either 0 or 1 for dead or alive) and then you use the results of the rules to set the new cell state.

How the 2 rule results are converted into a new cell state is the tricky part.

For two 2D CA rules there are 4 possible outcomes (dead dead, dead alive, alive dead, alive alive). So depending on the result states I have 4 options for alive or dead. This gives the following options.

Multiple Rules Cellular Automaton

Here are a few sample results after trying hundreds of random rules.

Multiple Rules Cellular Automaton

Multiple Rules Cellular Automaton

Multiple Rules Cellular Automaton

Multiple Rules Cellular Automaton

I will update this post if I find any more interesting results. There are many more extensions to try like 3D, 4D, 5D, larger neighborhoods, more than 2 rules, etc.

I did try the 3D version of multiple rules. Nothing worth posting as yet. I used both the result1 and result2 method above and also tried feeding the result of rule1 into rule2. Neither has given any unique looking results yet.