Genetic programming and cellular automata for fast flood modelling on multi-core CPU and many-core GPU computers

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

  title =        "Genetic programming and cellular automata for fast
                 flood modelling on multi-core {CPU} and many-core {GPU}
  author =       "Michael John Gibson",
  year =         "2015",
  school =       "University of Exeter",
  address =      "UK",
  month =        "24 " # aug,
  keywords =     "genetic algorithms, genetic programming, GPU",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "257 pages",
  abstract =     "Many complex systems in nature are governed by simple
                 local interactions, although a number are also
                 described by global interactions. For example, within
                 the field of hydraulics the Navier-Stokes equations
                 describe free-surface water flow, through means of the
                 global preservation of water volume, momentum and
                 energy. However, solving such partial differential
                 equations (PDEs) is computationally expensive when
                 applied to large 2D flow problems. An alternative which
                 reduces the computational complexity, is to use a local
                 derivative to approximate the PDEs, such as finite
                 difference methods, or Cellular Automata (CA). The high
                 speed processing of such simulations is important to
                 modern scientific investigation especially within urban
                 flood modelling, as urban expansion continues to
                 increase the number of impervious areas that need to be
                 modelled. Large numbers of model runs or large spatial
                 or temporal resolution simulations are required in
                 order to investigate, for example, climate change,
                 early warning systems, and sewer design optimisation.
                 The recent introduction of the Graphics Processor Unit
                 (GPU) as a general purpose computing device (General
                 Purpose Graphical Processor Unit, GPGPU) allows this
                 hardware to be used for the accelerated processing of
                 such locally driven simulations. A novel CA
                 transformation for use with GPUs is proposed here to
                 make maximum use of the GPU hardware. CA models are
                 defined by the local state transition rules, which are
                 used in every cell in parallel, and provide an
                 excellent platform for a comparative study of possible
                 alternative state transition rules. Writing local state
                 transition rules for CA systems is a difficult task for
                 humans due to the number and complexity of possible
                 interactions, and is known as the inverse problem for
                 CA. Therefore, the use of Genetic Programming (GP)
                 algorithms for the automatic development of state
                 transition rules from example data is also investigated
                 in this thesis. GP is investigated as it is capable of
                 searching the intractably large areas of possible state
                 transition rules, and producing near optimal solutions.
                 However, such population-based optimisation algorithms
                 are limited by the cost of many repeated evaluations of
                 the fitness function, which in this case requires the
                 comparison of a CA simulation to given target data.
                 Therefore, the use of GPGPU hardware for the
                 accelerated learning of local rules is also developed.
                 Speed-up factors of up to 50 times over serial Central
                 Processing Unit (CPU) processing are achieved on simple
                 CA, up to 5-10 times speedup over the fully parallel
                 CPU for the learning of urban flood modelling rules.
                 Furthermore, it is shown GP can generate rules which
                 perform competitively when compared with human
                 formulated rules. This is achieved with generalisation
                 to unseen terrains using similar input conditions and
                 different spatial/temporal resolutions in this
                 important application domain.",
  notes =        "British Library, EThOS",

Genetic Programming entries for Michael J Gibson