Abstract
An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.
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Correia, J., Machado, P., Romero, J., Carballal, A. (2013). Feature Selection and Novelty in Computational Aesthetics. In: Machado, P., McDermott, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2013. Lecture Notes in Computer Science, vol 7834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36955-1_12
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DOI: https://doi.org/10.1007/978-3-642-36955-1_12
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