Evolving aesthetic images using multiobjective optimization
Inhaltsverzeichnis
Reference
Greenfield, G.: Evolving aesthetic images using multiobjective optimization. In: Sarker, R., et al. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation, IEEE Press, Canberra.
DOI
http://dx.doi.org/10.1109/CEC.2003.1299906
Abstract
We consider the problem of using evolutionary multiobjective optimization to evolve visual imagery. In our method, images (phenomes) are generated from expressions (genomes), and then color segmented so that they can be evaluated under a number of different aesthetic criteria. Our principal task is to formulate fitness functions that make the best use of these elementary aesthetic components. We demonstrate the benefits obtained from using more than one objective function. We also discuss technical issues that arose as a consequence of treating our computational aesthetics problem as a "real-world" application of evolutionary multiobjective optimization.
Extended Abstract
Bibtex
Used References
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