Evolutionary optimization of interdisciplinary methodological strategies using Neutrosophic Genetic Algorithms to develop artistic drawing based on text comprehension

Abstract

This research constructs aims to address a current pedagogical issue in art teaching—what are the most effective and efficient interdisciplinary methodological combinations for development of art drawing from a text based on understanding the text. This is a current problem of pedagogical practice as it applies to today's classrooms because the simultaneous combination of linguistic and artistic, expressive capabilities will help cultivate the diversification of creative and critical thinking processes. This problem is not extensively explored within the literature—but noted gaps and inadequacies suggest that such an interdisciplinary approach has not been assessed, especially since few researchers seek to analyze factors like complexity, uncertainty and ambiguity. Therefore, to assess the problem and fill in the gaps, this study uses the Neutrosophic Genetic Algorithm approach through an interdisciplinary framework. The Neutrosophic Genetic Algorithm will not only enable the filtration of effective and efficient combinations through thousands of interdisciplinary variables simultaneously—but also, allow for the assessment of uncertainty related to the experts' findings. Ultimately, not only were the changes to combinations produce a statistically significant increase in effectiveness and efficiency of the combinations generated and scientifically proven—but also significant improvement in coherence and quality of creativity for art drawing development from texts. The theoretical contribution is using neutrosophics for interdisciplinary assessment. The practical contribution is a replicable template for any similar pedagogical ventures in the intersections of language, art, and critical thinking.