Learning aesthetic judgements in evolutionary art systems

Abstract

Learning aesthetic judgements is essential for reducing users' fatigue in evolutionary art systems. Although judging beauty is a highly subjective task, we consider that certain features are important to please users. In this paper, we introduce an adaptive model to learn aesthetic judgements in the task of interactive evolutionary art. Following previous work, we explore a collection of aesthetic measurements based on aesthetic principles. We then reduce them to a relevant subset by feature selection, and build the model by learning the features extracted from previous interactions. To apply a more accurate model, multi-layer perceptron and C4.5 decision tree classifiers are compared. In order to test the efficacy of the approach, an evolutionary art system is built by adopting this model, which analyzes the user's aesthetic judgements and approximates their implicit aesthetic intentions in the subsequent generations. We first tested these aesthetic measurements on different artworks from our selected artists. Then, a series of experiments were performed by a group of users to validate the adaptive learning model. The study reveals that different features are useful for identifying different patterns, but not all are relevant for the description of artists' styles. Our results show that the use of the learning model in evolutionary art systems is sound and promising for predicting users' preferences.