Computational Theory of AI Copulation and Hybridization: Self-Tuning Ecological Breeding of AI Populations

This paper presents an empirically validated computational theory for the breeding of artificial intelligence. We move beyond static fine-tuning to demonstrate a "Self-Tuning Ecological Breeding" framework where agents evolve via a 20-dimensional continuous epigenetic genotype (τ). Using the Gemma 2B model as a somatic substrate, we show that complex philosophical orientations can be successfully evolved and stored in a 1.7 KB footprint. Our results from two population scales (N = 99 and N = 60) demonstrate that larger populations sustain higher evolutionary momentum, reaching a peak activation mean of 1.5621 over 50 generations. This proves an unprecedented 1:100,000,000 parameter-to impact ratio, offering a highly efficient and transparent alternative to traditional alignment methods.