Learning aesthetic judgements in evolutionary art systems
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- @Article{Li:2013:GPEM,
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author = "Yang Li and Changjun Hu and Leandro L. Minku and
Haolei Zuo",
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title = "Learning aesthetic judgements in evolutionary art
systems",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2013",
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volume = "14",
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number = "3",
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pages = "315--337",
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month = sep,
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note = "Special issue on biologically inspired music, sound,
art and design",
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keywords = "genetic algorithms, genetic programming, Evolutionary
art, Interactive evolutionary computation, IEC, Image
complexity, Fractal compression",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-013-9188-7",
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language = "English",
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size = "23 pages",
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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 analyses
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",
- }
Genetic Programming entries for
Yang Li
Changjun Hu
Leandro L Minku
Haolei Zuo
Citations