Evaluation and improvement of the creativity of works of art by integrating artificial immune algorithm

Abstract

The creativity of artistic works encompasses multifaceted elements such as color, shape, and texture, characterized by their complexity and subtlety. Current evaluation methods are predominantly subjective, lacking the ability to objectively and comprehensively capture and quantify these dynamic attributes. To address this limitation, this study proposes an innovative approach for evaluating artistic creativity by integrating an artificial immune algorithm (AIA). Initially, features related to color, shape, and texture are extracted from the artworks, and these feature vectors are input into the AIA as antigens. Subsequently, by defining antigen-antibody matching rules, the features of the artworks are compared with creative reference antibodies generated by the algorithm to derive creativity evaluation results. Finally, leveraging a dynamic adjustment mechanism for bidirectional crossover mutation probabilities, the diversity of the antibody population is optimized through a clonal selection strategy, enhancing the model’s adaptability to diverse artistic styles. Experimental results demonstrate that the proposed improved AIA achieves significant enhancements in evaluation metrics, with average Precision and Recall values increasing by 8.45% and 11.21%, respectively, compared to the baseline AIA. This study concludes that the AIA-integrated creativity evaluation method effectively improves the objectivity and accuracy of artistic assessments, offering a novel perspective for the intelligent evaluation of artworks.