Accelerating Genetic Programming Using Graphics Processing Units

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Lewis:thesis,
  author =       "Tony Lewis",
  title =        "Accelerating Genetic Programming Using Graphics
                 Processing Units",
  school =       "Department of Computer Science and Information Systems
                 Birkbeck, University of London",
  year =         "2011",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, GPU, GPGPU,
                 TMBL, CUDA, PTX, nVidia, Cartesian Genetic Programming,
                 cyclic CGP, UML",
  URL =          "http://www.dcs.bbk.ac.uk/research/recentphds.php",
  URL =          "http://www.dcs.bbk.ac.uk/research/recentphds/tonylewis.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.5163",
  size =         "241 pages",
  abstract =     "Evolution through natural selection offers the
                 possibility of automatically generating functionally
                 complex solutions to a wide range of problems. Methods
                 such as Genetic Programming (GP) show the promise of
                 this approach but tend to stagnate after relatively few
                 generations. To research this issue, execution speed
                 must be substantially improved. This thesis presents
                 work to accelerate the execution of such methods.

                 The work uses the Graphics Processing Unit (GPU) to
                 target the evaluation of individuals since this is the
                 most time-consuming part of the run. Two models have
                 been emerging for this: dynamically compiling each new
                 generation of individuals for the GPU or using a single
                 GPU interpreter, to which successive groups of
                 individuals can be sent.

                 Using the latter model, a GPU interpreter is
                 constructed to implement cyclic GP, an advanced form of
                 GP that imposes several challenging implementation
                 issues which are addressed. Accelerating the evaluation
                 using the GPU is only part of the story. The next part
                 of the work interleaves CPU and GPU computation to keep
                 both chips as busy as possible with the tasks to which
                 they are best suited and then to recruit multiple GPUs
                 and CPU cores to further accelerate the run.

                 Using the former model, a compiling system is
                 constructed and this is used to investigate two methods
                 to overcome the primary difficulty with the approach:
                 long compilation times. That system implements Tweaking
                 Mutation Behaviour Learning (TMBL), a form focused on
                 long term fitness growth and overcoming the previously
                 mentioned stagnation issues. Further work optimises two
                 CPU tasks highlighted by profiling: tournament
                 selection and individual copying.

                 These techniques are highly effective and permit much
                 shorter run-times. This clears the way for research
                 into stimulating long term fitness growth and hence for
                 tackling new, complex problems.",
  notes =        "supervisor, George Magoulas",
}

Genetic Programming entries for Tony Lewis

Citations