Constructing an Optimisation Phase Using Grammatical Evolution

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

@InProceedings{Alexander:2009:cec,
  author =       "B. J. Alexander and M. J. Gratton",
  title =        "Constructing an Optimisation Phase Using Grammatical
                 Evolution",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "1209--1216",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P395.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983083",
  size =         "8 pages",
  abstract =     "Optimising compilers present their authors with an
                 intractable design space. A substantial body of work
                 has used heuristic search techniques to search this
                 space for the purposes of adapting optimisers to their
                 environment. To date, most of this work has focused on
                 sequencing, tuning and guiding the actions of atomic
                 hand-written optimisation phases. In this paper we
                 explore the adaption of optimisers at a deeper level by
                 demonstrating that it is feasible to automatically
                 build a non-trivial optimisation phase, for a simple
                 functional language, using Grammatical Evolution. We
                 show that the individuals evolved compare well in
                 performance to a handwritten optimisation phase on a
                 range of benchmarks. We conclude with proposals of how
                 this work and its applications can be extended.",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, SBSE, evolutionary computation, functional
                 languages, grammars, optimising compilers, search
                 problems, atomic hand-written optimisation phases,
                 heuristic search techniques, intractable design space",
  notes =        "Adl, DMO, C-MPI, FPGA, Semantics of program preserved.
                 libGE, GAlib, effective crossover, Python, SWIG
                 Python/C++. Canonical code. Five second time limit.
                 Haskell. Training examples changed to 'provide traction
                 for the evolutionary process' p1213. 'Evolution of
                 fittest individuals highly discontinuous' p1214. Some
                 examples where GE is competitive with hand written
                 compiler optimisation, others less so. Evolved code
                 _not_ like human. Compiler output _is_ correct.
                 proof-of-concept.

                 CEC 2009 - A joint meeting of the IEEE, the EPS and the
                 IET. IEEE Catalog Number: CFP09ICE-CDR. Also known as
                 \cite{4983083}",
}

Genetic Programming entries for Brad Alexander Michael Gratton

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