On quantifying of learning creativity through simulation and modeling of swarm intelligence and Neural Networks

This research work presents a systematic investigational study of an interesting challenging phenomenon observed in natural world. Mainly, presented study concerned with conceptual interdisciplinary analysis and evaluation of quantified learning creativity phenomenon. Associated with diverse aspects of measurable behavioral learning performance. That's observed by two diverse natural biological systems' models (human & non-human creatures). Specifically, introduced study of two biological models consider comparison of quantified learning creativity phenomenon. That's observed during human interactive tutoring/learning processes with environment. Versus ecological behavioral learning of swarm intelligence agents (Ants), during performing foraging process. Furthermore, presented comparative study inspired by naturally realistic models of Artificial Neural Network (ANN) and Swarm Intelligence. Interestingly, obtained simulation and modeling results have announced that learning performance curves of either models behave with close similarity to each other. More precisely, analysis and evaluation of learning performance curves of two diverse biological models revealed that both obey exponentially decayed learning curves; following least mean square (LMS) error algorithm.