Aaron's code
SBIA '98 Proceedings of the 14th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Teaching Evolutionary Design Systems by Extending "Context Free"
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automatic invention of fitness functions with application to scene generation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Ludic considerations of tablet-based evo-art
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
Aesthetic learning in an interactive evolutionary art system
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
Comparing aesthetic measures for evolutionary art
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
No photos harmed/growing paths from seed: an exhibition
NPAR '12 Proceedings of the Symposium on Non-Photorealistic Animation and Rendering
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We describe the building of a library of 10,000 distinct abstract art images, and how these can be interpreted as describing the placement of objects in a scene for generative painting projects. Building the library to contain only markedly distinct images necessitated a machine learning approach, whereby two decision trees were derived to predict visual similarity in pairs of images. The first tree uses genotypical information to predict before image generation whether two images will be too similar. The second tree uses phenotypical information, namely how pairs of images differ when segmented using various distance thresholds. Taken together, the trees are highly effective at quickly predicting when two images are similar, and we used this in an evolutionary search where non-unique individuals are pruned, to build up the library. We show how the pruning approach can be used alongside a fitness function to increase the yield of images with certain properties, such as low/high colour variety, symmetry and contrast.