Automatic Machine Code Generation for a Transport Triggered Architecture using Cartesian Genetic Programming

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

@Article{journals/ijaras/WalkerLTTT12,
  author =       "James Alfred Walker and Yang Liu and 
                 Gianluca Tempesti and Jon Timmis and Andy M. Tyrrell",
  title =        "Automatic Machine Code Generation for a Transport
                 Triggered Architecture using Cartesian Genetic
                 Programming",
  journal =      "International Journal of Adaptive, Resilient and
                 Autonomic Systems",
  year =         "2012",
  volume =       "3",
  number =       "4",
  pages =        "32--50",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  ISSN =         "1947-9220",
  DOI =          "doi:10.4018/jaras.2012100103",
  bibdate =      "2013-03-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijaras/ijaras3.html#WalkerLTTT12",
  abstract =     "Transport triggered architectures are used for
                 implementing bio-inspired systems due to their
                 simplicity, modularity and fault-tolerance. However,
                 producing efficient, optimised machine code for such
                 architectures is extremely difficult, since
                 computational complexity has moved from the
                 hardware-level to the software-level. Presented is the
                 application of Cartesian Genetic Programming (CGP) to
                 the evolution of machine code for a simple
                 implementation of transport triggered architecture. The
                 effectiveness of the algorithm is demonstrated by
                 evolving machine code for a 4-bit multiplier with three
                 different levels of parallelism. The results show that
                 100percent successful solutions were found by CGP and
                 by further optimising the size of the solutions, it is
                 possible to find efficient implementations of the 4-bit
                 multiplier. Further analysis of the solutions showed
                 that use of loops within the CGP function set could be
                 beneficial and was demonstrated by repeating the
                 earlier 4-bit multiplier experiment with the addition
                 of a loop function.",
}

Genetic Programming entries for James Alfred Walker Yang Liu Gianluca Tempesti Jon Timmis Andrew M Tyrrell

Citations