Title of article
Random number generators in genetic algorithms for unconstrained and constrained optimization Original Research Article
Author/Authors
Andrea Reese، نويسنده ,
Issue Information
روزنامه با شماره پیاپی سال 2009
Pages
14
From page
679
To page
692
Abstract
Presented here is a genetic algorithm that computes an approximate solution to constrained and unconstrained global optimization problems. This technique has been implemented using several pseudo- and quasi-random number generators and the results of several test examples are presented. The performance of this technique is based on a ranked comparison of relative error.
Keywords
Relative error , Pseudo-random number , Constrained optimization , Quasi-random number , Genetic Algorithm , Unconstrained optimization
Journal title
Nonlinear Analysis Theory, Methods & Applications
Serial Year
2009
Journal title
Nonlinear Analysis Theory, Methods & Applications
Record number
861805
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