Evolutionary inference of biochemical reaction networks accelerated on graphics processing units

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Nobile:2013:HPCS,
  author =       "Marco S. Nobile",
  booktitle =    "International Conference on High Performance Computing
                 and Simulation (HPCS 2013)",
  title =        "Evolutionary inference of biochemical reaction
                 networks accelerated on graphics processing units",
  year =         "2013",
  month =        "1-5 " # jul,
  pages =        "668--670",
  note =         "Doctoral Dissertation Colloquium",
  keywords =     "genetic algorithms, genetic programming, Systems
                 Biology, Reverse Engineering, Particle Swarm
                 Optimisation, GPGPU",
  DOI =          "doi:10.1109/HPCSim.2013.6641490",
  abstract =     "The reverse engineering (RE) of biochemical reaction
                 networks is a fundamental and very complex task in
                 Systems Biology. My PhD thesis is focused on the
                 definition of an automatic RE methodology based on the
                 fusion of Genetic Programming and Particle Swarm
                 Optimisation. The methodology I propose relies on the
                 execution of a massive number of simulations, whose
                 computational costs are relevant. To the aim of
                 reducing the overall running time, I am implementing
                 the methodology on a parallel architecture, namely,
                 Nvidia's CUDA.",
  notes =        "Also known as \cite{6641490}",
}

Genetic Programming entries for Marco Nobile

Citations