Genetically controlled random search: a global optimization method for continuous multidimensional functions

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@Article{Tsoulos:Gcr:06,
  author =       "Ioannis G. Tsoulos and Isaac E. Lagaris",
  title =        "Genetically controlled random search: a global
                 optimization method for continuous multidimensional
                 functions",
  journal =      "Computer Physics Communications",
  year =         "2006",
  volume =       "174",
  number =       "2",
  pages =        "152--159",
  month =        "15 " # jan,
  keywords =     "genetic algorithms, genetic programming, Global
                 optimisation, Stochastic methods, Grammatical
                 evolution",
  URL =          "http://www.cs.uoi.gr/~lagaris/papers/GCRS.pdf",
  DOI =          "doi:10.1016/j.cpc.2005.09.007",
  abstract =     "A new stochastic method for locating the global
                 minimum of a multidimensional function inside a
                 rectangular hyperbox is presented. A sampling technique
                 is employed that makes use of the procedure known as
                 grammatical evolution. The method can be considered as
                 a {"}genetic{"} modification of the Controlled Random
                 Search procedure due to Price. The user may code the
                 objective function either in C++ or in Fortran 77. We
                 offer a comparison of the new method with others of
                 similar structure, by presenting results of
                 computational experiments on a set of test
                 functions.

                 Program summary

                 Title of program: GenPrice

                 Catalogue identifier:ADWP

                 Program summary URL:
                 http://cpc.cs.qub.ac.uk/summaries/ADWP

                 Program available from: CPC Program Library, Queen's
                 University of Belfast, N. Ireland

                 Computer for which the program is designed and others
                 on which it has been tested: the tool is designed to be
                 portable in all systems running the GNU C++
                 compiler

                 Installation: University of Ioannina,
                 Greece

                 Programming language used: GNU-C++, GNU-C, GNU
                 Fortran-77

                 Memory required to execute with typical data: 200
                 KB

                 No. of bits in a word: 32

                 No. of processors used: 1

                 Has the code been vectorised or parallelised?: no

                 No. of lines in distributed program, including test
                 data, etc.:13 135

                 No. of bytes in distributed program, including test
                 data, etc.: 78 512

                 Distribution format: tar.gz

                 Nature of physical problem: A multitude of problems in
                 science and engineering are often reduced to minimising
                 a function of many variables. There are instances that
                 a local optimum does not correspond to the desired
                 physical solution and hence the search for a better
                 solution is required. Local optimisation techniques are
                 frequently trapped in local minima. Global optimization
                 is hence the appropriate tool. For example, solving a
                 nonlinear system of equations via optimisation,
                 employing a {"}least squares{"} type of objective, one
                 may encounter many local minima that do not correspond
                 to solutions, i.e. minima with values far from
                 zero.

                 Method of solution: Grammatical Evolution is used to
                 accelerate the process of finding the global minimum of
                 a multidimensional, multimodal function, in the
                 framework of the original {"}Controlled Random
                 Search{"} algorithm.

                 Typical running time: Depending on the objective
                 function.",
  notes =        "PACS: 02.60.-x; 02.60.Pn; 07.05.Kf; 02.70.Lq;
                 07.05.Mh",
}

Genetic Programming entries for Ioannis G Tsoulos Ioannis G Tsoulos

Citations