Random number generators in genetic algorithms for unconstrained and constrained optimization

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.

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