On Comparative Study for Quantified Learning Creativity Versus Behavioral Swarm Intelligence (Neural Networks’ Approach)

Abstract

This piece of research introduces an investigational systematic study for two diverse interdisciplinary, and challenging issues. More precisely, these issues are observed in natural real world, and concerned with two biological systems: humans' learning creativity, and social insects' behavioral intelligence. By some details this research article presents the conceptual analysis and evaluation of quantified learning creativity, and Swarm Intelligence phenomena via simulation and modeling of the two natural biological systems (human & non-human creatures). At one hand, analytical study that considers the Artificial Neural Networks (ANN$^{\underline{s}}$) modeling is adopted during solving of Optical Character Recognition (OCR) problem. However, on the other hand, the presented study deals with the optimal solution of Travelling Sales-man Problem (TSP) based on ecological behavioral learning of Swarm Intelligence (SI) agents (Ant mates), during performing foraging processes. Interestingly, both of the diverse creativity, and intelligence issues are realistically simulated using ANN$^{\underline{s}}$ supervised learning modeling (Error correction learning rule). Furthermore, the effect of noisy environmental nature on the learning performance as well as the intelligent, has been studied for both adopted issues respectively. Conclusively, presented results herein, for both swarm intelligence and neural networks models seemed to be well promising for future more elaborate, systematic, and innovative research in evaluation of human learning creativity phenomenon regarding the research in natural computing. That is genuinely interdisciplinary and forms a bridge between the natural sciences and computer science. This bridge connects the two, both at the level of information technology and at the level of fundamental research.