Investigating Aesthetic Features to Model Human Preference in Evolutionary Art
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Li:2012:EvoMUSART,
-
author = "Yang Li and Changjun Hu and Ming Chen and
Jingyuan Hu",
-
title = "Investigating Aesthetic Features to Model Human
Preference in Evolutionary Art",
-
booktitle = "Proceedings of the 1st International Conference on
Evolutionary and Biologically Inspired Music, Sound,
Art and Design, EvoMUSART 2012",
-
year = "2012",
-
month = "11-13 " # apr,
-
editor = "Penousal Machado and Juan Romero and
Adrian Carballal",
-
series = "LNCS",
-
volume = "7247",
-
publisher = "Springer Verlag",
-
address = "Malaga, Spain",
-
pages = "153--164",
-
organisation = "EvoStar",
-
keywords = "genetic algorithms, genetic programming, Aesthetic
learning, evolutionary art, interactive evolutionary
computation, computational aesthetics",
-
isbn13 = "978-3-642-29141-8",
-
DOI = "doi:10.1007/978-3-642-29142-5_14",
-
abstract = "In this paper we investigate aesthetic features in
learning aesthetic judgements in an evolutionary art
system. We evolve genetic art with our evolutionary art
system, BioEAS, by using genetic programming and an
aesthetic learning model. The model is built by
learning both phenotype and genotype features, which we
extracted from internal evolutionary images and
external real world paintings, which could lead to more
interesting paths. By learning aesthetic judgment and
applying the knowledge to evolve aesthetical images,
the model helps user to automate the process of
evolutionary process. Several independent experimental
results show that our system is efficient to reduce
user fatigue in evolving art.",
-
notes = "Part of \cite{Machado:2012:EvoMusArt_proc}
EvoMUSART'2012 held in conjunction with EuroGP2012,
EvoCOP2012, EvoBIO2012 and EvoApplications2012",
- }
Genetic Programming entries for
Yang Li
Changjun Hu
Ming Chen
Jingyuan Hu
Citations