Abstract: A system and method for implementing and training network-based artificial intelligence functions is provided, wherein the signals within an artificial neural network are encoded as strings of symbols such as alphanumeric characters, rather than as numerical weighting values, and accordingly propagated by table functions rather than by summation or sigmoid functions used to handle weighting values. Further, a method is provided for training networks through an evolutionary winnowing and cross-hybridizing process. This different approach to artificial networking is inspired by the biology of genetics, combined with that of neurons, and is anticipated to improve the efficiency and nuance of AI modeling.