One of the general aims of evolutionary art research is to build a computer system capable of creating interesting, beautiful, or creative results, including images, videos, animations, text, and performances. In this context, it is crucial to understand how fitness is conceived and implemented to explore the “interestingness,” beauty, or creativity that the system is capable of. In this paper, we survey the recent research on fitness for evolutionary art related to aesthetics. We also cover research in the psychology of aesthetics, including relation between complexity and aesthetics, measures of complexity, and complexity predictors. We try to establish connections between human perception and understanding of aesthetics with current evolutionary techniques.
Over the last few years, numerous studies have been conducted that have sought to address automatic image classification. These approaches have used a variety of experimental sets of images from several photography sites. In this chapter, the authors look at some of the most widely used in the field of computational aesthetics as well as the capacity for generalization that each of them offers. Furthermore, a set of images built up by psychologists is described in order to predict perceptual complexity as assessed by a closed group of persons in a controlled experimental setup. Lastly, a new hybrid method is proposed for the construction of a set of images or a dataset for the assessment and classification of aesthetic criteria. This method brings together the advantages of datasets based on photography websites and those of a dataset where assessment is made under controlled experimental conditions.
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