GenMin: An enhanced genetic algorithm for global optimization

Published: 1 June 2008| Version 1 | DOI: 10.17632/xv9827vjc5.1
Ioannis G. Tsoulos, I.E. Lagaris


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)



Computational Physics, Computational Method