Towards highly optimized cartesian genetic programming: from sequential via SIMD and thread to massive parallel implementation

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Radek Hrbacek and Lukas Sekanina",
  title =        "Towards highly optimized cartesian genetic
                 programming: from sequential via SIMD and thread to
                 massive parallel implementation",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  editor =       "Christian Igel and Dirk V. Arnold and 
                 Christian Gagne and Elena Popovici and Anne Auger and 
                 Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and 
                 Kalyanmoy Deb and Benjamin Doerr and James Foster and 
                 Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and 
                 Hitoshi Iba and Christian Jacob and Thomas Jansen and 
                 Yaochu Jin and Marouane Kessentini and 
                 Joshua D. Knowles and William B. Langdon and Pedro Larranaga and 
                 Sean Luke and Gabriel Luque and John A. W. McCall and 
                 Marco A. {Montes de Oca} and Alison Motsinger-Reif and 
                 Yew Soon Ong and Michael Palmer and 
                 Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and 
                 Guenther Ruhe and Tom Schaul and Thomas Schmickl and 
                 Bernhard Sendhoff and Kenneth O. Stanley and 
                 Thomas Stuetzle and Dirk Thierens and Julian Togelius and 
                 Carsten Witt and Christine Zarges",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "1015--1022",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "",
  DOI =          "doi:10.1145/2576768.2598343",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Most implementations of Cartesian genetic programming
                 (CGP) which can be found in the literature are
                 sequential. However, solving complex design problems by
                 means of genetic programming requires parallel
                 implementations of search methods and fitness
                 functions. This paper deals with the design of highly
                 optimized implementations of CGP and their detailed
                 evaluation in the task of evolutionary circuit design.
                 Several sequential implementations of CGP have been
                 analyzed and the effect of various additional
                 optimizations has been investigated. Furthermore, the
                 parallelism at the instruction, data, thread and
                 process level has been applied in order to take
                 advantage of modern processor architectures and
                 computer clusters. Combinational adders and multipliers
                 have been chosen to give a performance comparison with
                 state of the art methods.",
  notes =        "Also known as \cite{2598343} GECCO-2014 A joint
                 meeting of the twenty third international conference on
                 genetic algorithms (ICGA-2014) and the nineteenth
                 annual genetic programming conference (GP-2014)",

Genetic Programming entries for Radek Hrbacek Lukas Sekanina