> Figuring out how to compact the total measure of fitness into a single float as the simulation progresses is non trivial. I've found that dynamically adapting

> Figuring out how to compact the total measure of fitness into a single float as the simulation progresses is non trivial. I've found that dynamically adapting the fitness function and simulation parameters over time is essential. RL style training can let you train against a vector of losses... Custom loss functions are a thing... > I've found that dynamically adapting the fitness function and simulation parameters over time is essential Did you just figure out epsilon greedy? - very well known technique... Spiking / time domain... Non linearities can already be captured by stacking linearly activated layers, or connecting layers. Time domain, CNNs.. RNNs.. ResNet, U-Net as (now almost ancient) examples cover alot of the same ground. 10K neuron networks are tiny. I dont know what your trying to accomplish but I would suggest reading more literature, because it sounds like your stuck in a local optima of old ideas...