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Neflux

AI Simulation

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I would like to create a simulation that combines genetic programming and neural networks (neuroevolution?). These are some examples:

Neuroevolution

Predator-Prey Dynamics

Long-Short Term Memory (also very cool graphics)

Pathfinding

So far I have found only one decent tutorial that mixes those two: Smart Sweepers. I studied this code and I tried to get the best from it but it's not enough.

I mean, I studied neural networks in general because I liked the concept and I understood everything (how come you don't start studying it with videos like these). Now I know how to implement them in different languages (python, c++ and c#). I also liked Genetic Programming but I feel like a need a proper tutorial before starting to use it in more advanced programs. By looking at various source codes I understood how it works (epoch, fitness..), and it won't be hard to find tutorials to improve. What I'm really missing is the graphical interface and I don't even know where to start. Should I try to find a graphics engine to implement? Or should I keep it minimal and maybe do it from scratch? Most importantly do you know how should I study these subjects in order to know how to make them cooperate properly in a more advanced project? Do you know tutorials suitable for this?

 

Tell me what you think

 

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All the elements you mention combined restricts what your project has to be.

 

Genetic  Programming would be something you do at the coarse level because a Neutral Network becomes irrelevant when the problem it is solving changes too much .     I could then see the GA setting up the parameters/coefficients defining a Neural Network 'build' (the node layer definition, the training coefficients, the situational interpretation rules/the good/bad result judgement functions, training set selection), which then is applied to build a candidate NN training session (building a NN doing the usual reinforced learning method ).    A 'swarm' (swarm learning) of candidates are done with 'genetic' variations and a subset of the 'best' resulting NN then selected as the basis of the next iteration set of GA variations (working toward some 'bestest'  resulting NN)

 

How much you can automate building the training sets depends on the game mechanics of the simulation. (whether it can be subjected to the GA methodology)   Training sets can be quite large to cover the spectrum of cases the NN is expected to handle.  If they have to be hand built that is a seperate problem in the project (maybe GA can be used to assist in the training set building as an independant part of this project)

 

Swarm intelligence GA variation control factors itself is something that might be manipulated at a super coarse level.

 

Get ready for some huge amount of processing  - building just one NN can be a mass of computing, and multiple iterations of GA variations  multiplies that by magnitudes.

Edited by wodinoneeye

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