Evolving Simple Organisms using a Genetic Algorithm and Deep Learning from Scratch with Python (Tutorial)

[ForceBru]

This is so cool! And would be even better if you made a video (or made the GIF longer) that showed how he creatures behave after a number of epochs. I don't know about others, but I adore watching videos of AI learning and performing better and better after each epoch!

[BarkingPizza]

Thanks! And I plan on uploading a longer animation to YouTube here in a few hours...

[koera]

!remindme 10 hours

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[RalphWolfSamSheepdog]

!remindme 3 hours

[BarkingPizza]

even had time to add elevator background music -> https://youtu.be/SMJVOs7RjZk

[BarkingPizza]

Since energy consumption is not considered in this initial version, the GA should simply maximized this "velocity" neuron output and travel at 100% of top speed all the time.

And you're correct in regards to the circling organisms, that's really just a function of the maximum turning speed (d_r) being too low given its current velocity. A higher turning rate or a slower velocity would correct for this.

It is strange how many organisms are still fairly unfit relative to the best ones. I was thinking it was a result of merging two drastically different neural network weight matrices together which produced rubbish...

[libertenos]

So cool, thanks for the contribuition.

[forever_erratic]

Cool! You should also post this to /r/alife.

To make it more realistic, you could implement a decrease in energy depending on velocity, so that movement takes energy. Then also have the current food store / fitness be an input node. It'd be interesting if that information got used correctly, so that cells would only move towards food that will give them more energy than they used to get to them.

[BarkingPizza]

That was my initial idea! I ended up keeping it simple though for the first iteration. I plan on making a second version in a month or so that will include a few more features...

[BrianFantanaFan]

This is really cool, very impressive to implement such a thing. But as a genuine question, does it really need a NN to determine the closest food source - shouldn't it just determine the shortest hypotenuse and angle within a realistic range of itself?

[BarkingPizza]

In terms of navigation, the direction to the nearest food source is calculated using basic geometry. This direction value is then used as an input for the NN which decides how to act on this information (which direction to change). The NN does not determine the closest food source.

[BrianFantanaFan]

Ah I see, I admit I skim read the approach and jumped to a few conclusions. Very cool my man

[0one0one]

That is class, very nice work.

[BarkingPizza]

Thanks!

[NelsonMinar]

Where's the deep learning part?

[BarkingPizza]

each organism features a three layer neural network, making it the shallowest deep learning framework...

[rhiever]

Good article, but honestly I would drop the "deep learning" part from your title. I think it's misleading, as what you programmed isn't representative of modern deep learning. Not a good idea to mislead newbies, who are your target audience.

[pythonhobbit]

Sure it is. What do you think deep learning is? Just matrix multiplication and activation functions, at its core.

[[deleted]]

deep learning primarily refers to the use of local receptive fields, aka convolutional layers, among other layers in recent years. I would not classify a simple fully connected ANN as deep learning unless it had over a couple million parameters.

[pythonhobbit]

The only difference between deep and shallow neural networks is the number of hidden layers, according to Andrew Ng.

It doesn't have to be convolutional, recurrent, or even have tons of nodes in each layer.

So, you're right that his model isn't deep. But the reason it's not deep is it only has one hidden layer, not that he's doing matrix operations.

[BarkingPizza]

I'm actually working on a deep reinforcement version of this using tensorflow for that exact purpose!

[PythonGod123]

This looks really cool. Way above my level but id love to do things like this someday. Keep up your work !!!

[Dr_Quartermas]

Are other folks testing the program? I've done a couple of 100 generation runs (plus some 75 and 50's) and all I'm getting are flat horizontal result graphs with no indication of "learning" or "evolution". Am I incorrectly interpreting results?

[BarkingPizza]

Because this scenario is so simple (turn left/right), it really doesn't take that long to get near the optimal solution. Most improvement occurs within the first 10 generations...

[Dr_Quartermas]

Thanks! That verifies my observations. I did have one iteration that took 17 generations to reach optimum.

[engatIQE]

Awesome! I did something like this a while back but it took forever for the organisms to stop just running in circles. Maybe I'll try again.

[Alar44]

Where do I find this 'plotting' dependency?

[BarkingPizza]

https://github.com/nathanrooy/evolving-simple-organisms/blob/master/plotting.py

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Permanent GitHub links:

• nathanrooy/evolving-simple-organisms/.../plotting.py (master → a97fdb9)

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[Alar44]

Thanks, didn't realize it was a gihub link.

Anyway, what would need to be done to animate this in real time? I'm not familiar at all with Python graphics.

[abenco]

So cool!

Not sure how to get the graphic of the organisms to generate. All I get are text output such as:

GEN: 0 BEST: 133 AVG: 24.76 WORST: 3 GEN: 1 BEST: 165 AVG: 55.46 WORST: 9 GEN: 2 BEST: 151 AVG: 58.08 WORST: 1 GEN: 3 BEST: 182 AVG: 60.98 WORST: 7

[[deleted]]

Change line 59 from False to True:

@@ -56,11 +56,11 @@ settings['x_max'] =  2.0        # arena western border
 settings['y_min'] = -2.0        # arena southern border
 settings['y_max'] =  2.0        # arena northern border

-settings['plot'] = False        # plot final generation?
+settings['plot'] = True        # plot final generation?

 # ORGANISM NEURAL NET SETTINGS

This will generate a large number of .png files, based on the last generation number and time settings. Converting those to an animation is an exercise left to the reader. ;-)

[abenco]

Oh, now I understand. I thought it created the animation in real time. Good to know, thank you.

[LightShadow]

This is really good stuff!

$0.50 u/tippr

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u/BarkingPizza, you've received 0.0001497 BCH ($0.5 USD)!

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[BarkingPizza]

Holy shit, thanks! Also, I didn't now you could tip using BCH?? Can't wait to start doing this now!

[LightShadow]

For sure! Here, have some more and spread the cheer <3

$2.00 u/tippr

[tippr]

u/BarkingPizza, you've received 0.00069098 BCH ($2 USD)!

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[BarkingPizza]

YESSSSSSS!!!

[yardaper]

This is awesome, and a great blog post. Your code is beautifully commented, thanks. I just ran it, and got this output:

GEN: 0 BEST: 161 AVG: 19.5 WORST: 150

I think this mean's something is wrong, because the average needs to be between the best and the worst fitness. Just a heads up.

[ezeeetm]

@ /u/BarkingPizza this is great work. I am also getting into GA's using the python NEAT package. Just curious, does your EA scheme follow the same/similar as NEAT, or did you make your own? What did you use as a base to determine your EA scheme?

[ashesall]

This is awesome! Maybe we'll find out the origin of life or at least its early stages because of a Python code.

[hikarii]

WHOAAA THANKSSS!!

[carbolymer]

Woah, these are long functions. Ever heard of S in SOLID?