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- W2185858132 abstract "Autonomous aesthetic evaluation is the Holy Grail of generative music, and one of the great challenges of computational creativity. Unlike most other computational activities, there is no notion of optimality in evaluating creative output: there are subjective impressions involved, and framing obviously plays a big role. When developing metacreative systems, a purely objective fitness function is not available: the designer is thus faced with how much of their own aesthetic to include. Can a generative system be free of the designer’s bias? This paper presents a system that incorporates an aesthetic selection process that allows for both human-designed and non-human fitness functions." @default.
- W2185858132 created "2016-06-24" @default.
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- W2185858132 date "2012-10-09" @default.
- W2185858132 modified "2023-09-23" @default.
- W2185858132 title "Embracing the Bias of the Machine: Exploring Non-Human Fitness Functions" @default.
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- W2185858132 doi "https://doi.org/10.5281/zenodo.4285436" @default.
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