- author = "Ioannis G. Tsoulos and I. E. Lagaris",
- title = "{GenMin:} An enhanced genetic algorithm for global optimization",
- journal = "Computer Physics Communications",
- volume = "178",
- number = "11",
- pages = "843--851",
- year = "2008",
- ISSN = "0010-4655",
-
DOI = "
doi:10.1016/j.cpc.2008.01.040",
-
URL = "
http://www.sciencedirect.com/science/article/B6TJ5-4RR8YW1-2/2/9b76a8b289abccf9ec864e11a54573a7",
- keywords = "genetic algorithms, genetic programming, Grammatical evolution, Global optimization, Stochastic methods, Stopping rule",
-
abstract = "A new method that employs grammatical evolution and a
stopping rule for finding the global minimum of a
continuous multidimensional, multimodal function is
considered. The genetic algorithm used is a hybrid
genetic algorithm in conjunction with a local search
procedure. We list results from numerical experiments
with a series of test functions and we compare with
other established global optimization methods. The
accompanying software accepts objective functions coded
either in Fortran 77 or in C++.
Program summary
Program title: GenMin
Catalogue identifier: AEAR_v1_0
Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html
Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland
Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html
No. of lines in distributed program, including test data, etc.: 35[thin space]810
No. of bytes in distributed program, including test data, etc.: 436[thin space]613
Distribution format: tar.gz
Programming language: GNU-C++, GNU-C, GNU Fortran 77
Computer: The tool is designed to be portable in all systems running the GNU C++ compiler
Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler
RAM: 200 KB
Word size: 32 bits
Classification: 4.9
Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing 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 optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero).
Solution method: Grammatical evolution and a stopping rule.
Running time: Depending on the objective function. The test example given takes only a few seconds to run.",
