Use of graphics processing units for automatic synthesis of programs

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

  author =       "Cleomar Pereira {da Silva} and Douglas {Mota Dias} and 
                 Cristiana Bentes and Marco Aurelio Cavalcanti Pacheco",
  title =        "Use of graphics processing units for automatic
                 synthesis of programs",
  journal =      "Computer \& Electrical Engineering",
  volume =       "46",
  pages =        "112--122",
  year =         "2015",
  ISSN =         "0045-7906",
  DOI =          "doi:10.1016/j.compeleceng.2015.04.006",
  URL =          "",
  abstract =     "Genetic programming (GP) is an evolutionary method
                 that allows computers to solve problems automatically.
                 However, the computational power required for the
                 evaluation of billions of programs imposes a serious
                 limitation on the problem size. This work focuses on
                 accelerating GP to support the synthesis of large
                 problems. This is done by completely exploiting the
                 highly parallel environment of graphics processing
                 units (GPUs). Here, we propose a new quantum-inspired
                 linear GP approach that implements all the GP steps in
                 the GPU and provides the following: (1) significant
                 performance improvements in the GP steps, (2)
                 elimination of the overhead of copying the fitness
                 results from the GPU to the CPU, and (3) incorporation
                 of a new selection mechanism to recognize the programs
                 with the best evaluations. The proposed approach
                 outperforms the previous approach for large-scale
                 synthetic and real-world problems. Further, it provides
                 a remarkable speedup over the CPU execution.",
  keywords =     "genetic algorithms, genetic programming, GPU
                 acceleration, Machine code, Quantum-inspired
                 algorithms, Massive parallelism",

Genetic Programming entries for Cleomar Pereira da Silva Douglas Mota Dias Cristiana Bentes Marco Aurelio Cavalcanti Pacheco