- 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",
