A Self-Scaling Instruction Generator Using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Yang Liu and Gianluca Tempesti and James A. Walker and 
                 Jon Timmis and Andrew M. Tyrrell and Paul Bremner",
  title =        "A Self-Scaling Instruction Generator Using Cartesian
                 Genetic Programming",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "298--309",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming: poster",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_26",
  abstract =     "In the past decades, a number of genetic programming
                 techniques have been developed to evolve machine
                 instructions. However, these approaches typically
                 suffer from a lack of scalability that seriously
                 impairs their applicability to real-world scenarios. In
                 this paper, a novel self-scaling instruction generation
                 method is introduced, which tries to overcome the
                 scalability issue by using Cartesian Genetic
                 Programming. In the proposed method, a dual-layer
                 network architecture is created: one layer is used to
                 evolve a series of instructions while the other is
                 dedicated to the generation of loop control
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and

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