A GPU-based implementation of an enhanced GEP algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Shuai Shao and Xiyang Liu and Mingyuan Zhou and 
                 Jiguo Zhan and Xin Liu and Yanli Chu and Hao Chen",
  title =        "A GPU-based implementation of an enhanced GEP
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "999--1006",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, GPU, CUDA, parallel
                 evolutionary systems",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330302",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Gene expression programming (GEP) is a functional
                 genotype/phenotype system. The separation scheme
                 increases the efficiency and reliability of GEP.
                 However, the computational cost increases considerably
                 with the expansion of the scale of problems. In this
                 paper, we introduce a GPU-accelerated hybrid variant of
                 GEP named pGEP (parallel GEP). In order to find the
                 optimal constant coefficients locally on the fixed
                 function structure, the Method of Least Square (MLS)
                 has been embedded into the GEP evolutionary process. We
                 tested pGEP using a broad problem set with a varying
                 number of instances. In the performance experiment, the
                 GPU-based GEP, when compared with the traditional GEP
                 version, increased speeds by approximately 250 times.
                 We compared pGEP with other well-known constant
                 creation methods in terms of accuracy, demonstrating
                 MLS performs at several orders of magnitude higher in
                 terms of both the best residuals and average
  notes =        "Also known as \cite{2330302} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Shuai Shao Xiyang Liu Mingyuan Zhou Jiguo Zhan Xin Liu Yanli Chu Hao Chen