Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
C4.5: programs for machine learning
C4.5: programs for machine learning
Frankensteinian methods for evolutionary music composition
Musical networks
Creative evolutionary systems
Evolutionary Design by Computers with CDrom
Evolutionary Design by Computers with CDrom
Applied Intelligence
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Procedural texture evolution using multi-objective optimization
New Generation Computing
ACM SIGGRAPH 2006 Papers
Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Incorporating characteristics of human creativity into an evolutionary art algorithm
Genetic Programming and Evolvable Machines
A corpus-based hybrid approach to music analysis and composition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Evolving art using multiple aesthetic measures
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Modelling human preference in evolutionary art
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Aesthetic learning in an interactive evolutionary art system
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Learning comparative user models for accelerating human-computer collaborative search
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Fitness in evolutionary art and music: what has been used and what could be used?
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
On the origins of the term "Computational aesthetics"
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Defining computational aesthetics
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Global contrast factor - a new approach to image contrast
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Benford's law for natural and synthetic images
Computational Aesthetics'05 Proceedings of the First Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Informational dialogue with van Gogh's paintings
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Hi-index | 0.00 |
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.