Abstract

Transgenetic algorithms can be used for performing a stochastic search by simulating endosymbiotic interactions between a host and a population of endosymbionts as well as information exchange between the host and endosymbionts by agents. The already introduced, computationally intelligent Data Mining system "System applying High Order Computational Intelligence in Data Mining” (SHOCID) applies such for Artificial Neural Network (ANN) learning by the combination of one of its learning approaches with a host organism, serving as genetic pool, and transgenetic vectors. The application of an algorithm combining horizontal gene transfer between a host and a symbiont is a completely new ANN learning approach, which increases both learning performance and accuracy to a considerable degree. A further advantage is that the application of transgenetic vectors massively increases the chance of reaching the desired stopping criteria (like a minimum Root Mean Squared Error [RMSE]) instead of abort criteria (like the evolutionary stop after 5,000 generations without improvement although the desired have not been fulfilled), as even learning algorithms like back propagation cannot oscillate or get stuck in local minima due to the inescapable transfer of host genetic material.