Comparison between Genetic Algorithm and Genetic Programming Performance for Photomosaic Generation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/seal/SahCDB08,
  author =       "Shahrul Badariah Mat Sah and Victor Ciesielski and 
                 Daryl J. D'Souza and Marsha Berry",
  title =        "Comparison between Genetic Algorithm and Genetic
                 Programming Performance for Photomosaic Generation",
  booktitle =    "Proceedings of the 7th International Conference on
                 Simulated Evolution And Learning (SEAL '08)",
  year =         "2008",
  editor =       "Xiaodong Li and Michael Kirley and Mengjie Zhang and 
                 David G. Green and Victor Ciesielski and 
                 Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and 
                 Kalyanmoy Deb and Kay Chen Tan and 
                 J{\"u}rgen Branke and Yuhui Shi",
  volume =       "5361",
  series =       "Lecture Notes in Computer Science",
  pages =        "259--268",
  address =      "Melbourne, Australia",
  month =        dec # " 7-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Photomosaic",
  isbn13 =       "978-3-540-89693-7",
  DOI =          "doi:10.1007/978-3-540-89694-4_27",
  abstract =     "Photomosaics are a new form of art in which smaller
                 digital images (known as tiles) are used to construct
                 larger images. Photomosaic generation not only creates
                 interest in the digital arts area but has also
                 attracted interest in the area of evolutionary
                 computing. The photomosaic generation process may be
                 viewed as an arrangement optimisation problem, for a
                 given set of tiles and suitable target to be solved
                 using evolutionary computing. In this paper we assess
                 two methods used to represent photomosaics, genetic
                 algorithms (GAs) and genetic programming (GP), in terms
                 of their flexibility and efficiency. Our results show
                 that although both approaches sometimes use the same
                 computational effort, GP is capable of generating finer
                 photomosaics in fewer generations. In conclusion, we
                 found that the GP representation is richer than the GA
                 representation and offers additional flexibility for
                 future photomosaics generation.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Shahrul Badariah Mat Sah Victor Ciesielski Daryl J D'Souza Marsha Berry

Citations