Evolving Reaction-Diffusion Systems on GPU

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Yamamoto:2011:EPIA,
  author =       "Lidia Yamamoto and Wolfgang Banzhaf and 
                 Pierre Collet",
  title =        "Evolving Reaction-Diffusion Systems on {GPU}",
  booktitle =    "Proceedings 15th Portuguese Conference on Artificial
                 Intelligence, {EPIA 2011}",
  year =         "2011",
  editor =       "Luis Antunes and Helena Sofia Pinto",
  volume =       "7026",
  series =       "Lecture Notes in Computer Science",
  pages =        "208–-223",
  address =      "Lisbon, Portugal",
  month =        oct # " 10-13",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, GPU",
  isbn13 =       "978-3-642-24768-2",
  DOI =          "doi:10.1007/978-3-642-24769-9_16",
  size =         "16 pages",
  abstract =     "Reaction-diffusion systems contribute to various
                 morphogenetic processes, and can also be used as
                 computation models in real and artificial chemistries.
                 Evolving reaction-diffusion solutions automatically is
                 interesting because it is otherwise difficult to
                 engineer them to achieve a target pattern or to perform
                 a desired task. However most of the existing work
                 focuses on the optimization of parameters of a fixed
                 reaction network. In this paper we extend this state of
                 the art by also exploring the space of alternative
                 reaction networks, with the help of GPU hardware. We
                 compare parameter optimization and reaction network
                 optimization on the evolution of reaction-diffusion
                 solutions leading to simple spot patterns. Our results
                 indicate that these two optimization modes tend to
                 exhibit qualitatively different evolutionary dynamics:
                 in the former, the fitness tends to improve
                 continuously in gentle slopes, while the latter tends
                 to exhibit large periods of stagnation followed by
                 sudden jumps, a sign of punctuated equilibria.",
  notes =        "Says GP analogue",
}

Genetic Programming entries for Lidia Yamamoto Wolfgang Banzhaf Pierre Collet

Citations