GenMin: An enhanced genetic algorithm for global optimization
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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 ...
Title of program: GenMin
Catalogue Id: AEAR_v1_0
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 non - linear system of equations via optimization, employing a least squares type of objectiv ...
Versions of this program held in the CPC repository in Mendeley Data
AEAR_v1_0; GenMin; 10.1016/j.cpc.2008.01.040
This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
创建时间：
2020-01-06
