Graph of Life

Hello everyone. I have been working on an evolutionary algorithm based on game theory and graph theory for three years now. In this algorithm complex life emerges through autonomous agents.The nodes are all individuals with their own neural networks. They see each other, make decisions and compete for scarce resources by attacking or defending. They evolve with natural selection and are self organizing. They decide themselves with who they want to interact or not. Reproduction happens at a local level and is dependant on the decisions of the agents. The algorithm happens in discrete iterations.

I‘m reaching out because I‘m a bit stuck currently. Originally the goal was to invent an algorithm where open ended evolution can occur, meaning that there is no optimal strategy, meaning that cooperations with ever encreasing complexity can emerge. The problem is that I don’t know how to falsify or prove this claim. The problem I have is that I don‘t know how to analyse this algorithm and the behaviors that emerge. I don‘t know how to find out what behaviors emerge and why other behaviors vanish. Also I don‘t know how I could quantify cooperation (if that happens at all).

Also one thought experiment that would be interesting: lets say intelligent life would emerge in this algorithm and they would do physics to find out how their reality works: what is the most fundamental thing they would be able to measure? I also don‘t know how to approach that, essentially it would be interesting to somehow interact with the algorithm and try to gain as much information as possible.

Also keep in mind that this is not just one algorithm, but a whole family of algorithms, that all work slightly differently. So the concept should in some way be general enough to be implemented for all cases.

Find the code at my github repository: https://github.com/graphoflife Find more videos at my instagram: https:// www.instagram.com/graph.of.life

Made with Axiom in Houdini 19.5

Hello everyone. I have been working on an evolutionary algorithm based on game theory and graph theory for three years now. In this algorithm complex life emerges through autonomous agents. The nodes are all individuals with their own neural networks. They see each other, make decisions and compete for scarce resources by attacking or defending. They evolve with natural selection and are self organizing. They decide themselves with who they want to interact or not. Reproduction happens at a local level and is dependant on the decisions of the agents. The algorithm happens in discrete iterations. Find the code at my github repository: https://github.com/graphoflife Find more videos at my instagram: https://www.instagram.com/graph.of.life