Working with OpenCL to speed up a genetic programming financial forecasting algorithm: initial results

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

@InProceedings{Brookhouse:2014:GECCOcomp,
  author =       "James Brookhouse and Fernando E. B. Otero and 
                 Michael Kampouridis",
  title =        "Working with {OpenCL} to speed up a genetic
                 programming financial forecasting algorithm: initial
                 results",
  booktitle =    "GECCO 2014 Workshop on Evolutionary Computation
                 Software Systems (EvoSoft)",
  year =         "2014",
  editor =       "Stefan Wagner and Michael Affenzeller",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming, GPU",
  pages =        "1117--1124",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "https://kar.kent.ac.uk/42144/",
  URL =          "http://doi.acm.org/10.1145/2598394.2605689",
  DOI =          "doi:10.1145/2598394.2605689",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The genetic programming tool EDDIE has been shown to
                 be a successful financial forecasting tool, however it
                 has suffered from an increase in execution time as new
                 features have been added. Speed is an important aspect
                 in financial problems, especially in the field of
                 algorithmic trading, where a delay in taking a decision
                 could cost millions. To offset this performance loss,
                 EDDIE has been modified to take advantage of multi-core
                 CPUs and dedicated GPUs. This has been achieved by
                 modifying the candidate solution evaluation to use an
                 OpenCL kernel, allowing the parallel evaluation of
                 solutions. Our computational results have shown
                 improvements in the running time of EDDIE when the
                 evaluation was delegated to the OpenCL kernel running
                 on a multi-core CPU, with speed ups up to 21 times
                 faster than the original EDDIE algorithm. While most
                 previous works in the literature reported significantly
                 improvements in performance when running an OpenCL
                 kernel on a GPU device, we did not observe this in our
                 results. Further investigation revealed that memory
                 copying overheads and branching code in the kernel are
                 potentially causes of the (under-)performance of the
                 OpenCL kernel when running on the GPU device.",
  notes =        "Also known as \cite{2605689} Distributed at
                 GECCO-2014.",
}

Genetic Programming entries for James Brookhouse Fernando Esteban Barril Otero Michael Kampouridis

Citations