Massively Parallel Genetic Programming on GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{daSilva:thesis,
  author =       "Cleomar Pereira {da Silva}",
  title =        "Programacao Genetica Macicamente Paralela em {GPUs}",
  title =        "Massively Parallel Genetic Programming on {GPUs}",
  school =       "Departamento de Engenharia Eletrica do Centro Tecnico
                 Cientifico da Pontificia Universidade Catolica do Rio
                 de Janeiro",
  year =         "2014",
  address =      "Brazil",
  month =        "11 " # sep,
  keywords =     "genetic algorithms, genetic programming, GPU, quantum
                 inspired, graphics processing units, machine code,
                 QILGP, CUBIN",
  URL =          "http://doi.org/10.17771/PUCRio.acad.24129",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_1.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_2.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_3.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_4.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_5.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_6.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_7.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_8.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_9.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_10.PDF",
  URL =          "http://www.maxwell.vrac.puc-rio.br/24129/24129_11.PDF",
  size =         "134 pages",
  abstract =     "Genetic Programming enables computers to solve
                 problems automatically, without being programmed to it.
                 Using the inspiration in the Darwin's Principle of
                 natural selection, a population of programs or
                 individuals is maintained, modified based on genetic
                 variation, and evaluated according to a fitness
                 function. Genetic programming has been successfully
                 applied to many different applications such as
                 automatic design, pattern recognition, robotic control,
                 data mining and image analysis. However, the evaluation
                 of the huge amount of individuals requires excessive
                 computational demands, leading to extremely long
                 computational times for large size problems. This work
                 exploits the high computational power of graphics
                 processing units, or GPUs, to accelerate genetic
                 programming and to enable the automatic generation of
                 programs for large problems. We propose two new
                 methodologies to exploit the power of the GPU in
                 genetic programming: intermediate language compilation
                 and individuals creation in machine language. These
                 methodologies have advantages over traditional methods
                 used in the literature. The use of an intermediate
                 language reduces the compilation steps, and works with
                 instructions that are well-documented. The individuals
                 creation in machine language has no compilation step,
                 but requires reverse engineering of the instructions
                 that are not documented at this level. Our
                 methodologies are based on linear genetic programming
                 and are inspired by quantum computing. The use of
                 quantum computing allows rapid convergence, global
                 search capability and inclusion of individuals' past
                 history. The proposed methodologies were compared
                 against existing methodologies and they showed
                 considerable performance gains. It was observed a
                 maximum performance of 2,74 trillion GPops (genetic
                 programming operations per second) for the 20-bit
                 Multiplexer benchmark, and it was possible to extend
                 genetic programming for problems that have databases
                 with up to 7 million samples.",
  abstract =     "A Programacao Genetica permite que computadores
                 resolvam problemas automaticamente, sem que eles tenham
                 sido programados para tal. Utilizando a inspiracao no
                 principio da selecao natural de Darwin, uma populacao
                 de programas, ou individuos, e mantida, modificada
                 baseada em variacao genetica, e avaliada de acordo com
                 uma funcao de aptidao (fitness). A programacao genetica
                 tem sido usada com sucesso por uma serie de aplicacoes
                 como projeto automatico, reconhecimento de padroes,
                 controle robotico, mineracao de dados e analise de
                 imagens. Porem, a avaliacao da gigantesca quantidade de
                 individuos gerados requer excessiva quantidade de
                 computacao, levando a um tempo de execucao inviavel
                 para problemas grandes. Este trabalho explora o alto
                 poder computacional de unidades de processamento
                 grafico, ou GPUs, para acelerar a programacao genetica
                 e permitir a geracao automatica de programas para
                 grandes problemas. Propomos duas novas metodologias
                 para se explorar a GPU em programacao genetica:
                 compilacao em linguagem intermediaria e a criacao de
                 individuos em codigo de maquina. Estas metodologias
                 apresentam vantagens em relacao as metodologias
                 tradicionais usadas na literatura. A utilizacao de
                 linguagem intermediaria reduz etapas de compilacao e
                 trabalha com instrucoes que estao bem documentadas. A
                 criacao de individuos em codigo de maquina nao possui
                 nenhuma etapa de compilacao, mas requer engenharia
                 reversa das instrucoes que nao estao documentadas neste
                 nivel. Nossas metodologias sao baseadas em programacao
                 genetica linear e inspiradas em computacao quantica. O
                 uso de computacao quantica permite uma convergencia
                 rapida, capacidade de busca global e inclusao da
                 historia passada dos individuos. As metodologias
                 propostas foram comparadas com as metodologias
                 existentes e apresentaram ganhos consideraveis de
                 desempenho. Foi observado um desempenho maximo de ate
                 2,74 trilhoes de GPops (operacoes de programacao
                 genetica por segundo) para o benchmark Multiplexador de
                 20 bits e foi possivel estender a programacao genetica
                 para problemas que apresentam bases de dados de ate 7
                 milhoes de amostras.",
  notes =        "In Portuguese.

                 2740000 MGPOPs Supervisor: Marco Aurelio Cavalcanti
                 Pacheco, Co-Orientador: Douglas Mota Dias,
                 Co-Orientador: Cristiana Barbosa Bentes",
}

Genetic Programming entries for Cleomar Pereira da Silva

Citations