Estimating and predicting average likability on computer-generated artwork variants

  • Jabier Martinez
  • , Gabriele Rossi
  • , Tewfik Ziadi
  • , Tegawendé F. Bissyandé
  • , Jacques Klein
  • , Yves Le Traon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Computer assisted human creativity encodes human design decisions in algorithms allowing machines to produce artwork variants. Based on this automated production, one can leverage collective understanding of beauty to rank computergenerated artworks according to their average likability. We present the use of Software Product Line techniques for computer-generated art systems as a case study on leveraging the feedback of human perception within the boundaries of a variability model. Since it is not feasible to get feedback for all variants because of a combinatorial explosion of possible configurations, we propose an approach that is developed in two phases: 1) the creation of a data set using an interactive genetic algorithm and 2) the application of a data mining technique on this dataset to create a ranking enriched with confidence metrics.

Original languageEnglish
Title of host publicationGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference
EditorsSara Silva
PublisherAssociation for Computing Machinery, Inc
Pages1431-1432
Number of pages2
ISBN (Electronic)9781450334884
DOIs
Publication statusPublished - 11 Jul 2015
Externally publishedYes
Event17th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015

Publication series

NameGECCO 2015 - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference

Conference

Conference17th Genetic and Evolutionary Computation Conference, GECCO 2015
Country/TerritorySpain
CityMadrid
Period11/07/1515/07/15

Keywords

  • Computer-generated art
  • Data mining
  • Interactive genetic algorithms
  • Software product lines
  • User feedback

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