Evolving "Transparent Intelligence" with <100 Weights – It Failed

I ran an experiment to evolve what I call “transparent intelligence” using fewer than 100 neural network weights. Using a genetic algorithm over 3000+ generations, survival improved from 0% to around 50%. However, true discrimination behavior — distinguishing food from poison in the environment — never emerged. After extensive parameter tuning and repeated trials, I concluded that the limitation was likely not hyperparameter tuning, but the representation and the structure of the search space itself. I’ve documented the full process, including the failed attempts and analysis.