  The history of research on eigenvalue problems is rich with many outstanding
contributions. Nonetheless, the rapidly increasing size of data sets requires
new algorithms for old problems in the context of extremely large matrix
dimensions. This paper reports on a new method for finding eigenvalues of very
large matrices by a synthesis of evolutionary computation, parallel
programming, and empirical stochastic search. The direct design of our method
has the added advantage that it could be adapted to extend many algorithmic
variants of solutions of generalized eigenvalue problems to improve the
accuracy of our algorithms. The preliminary evaluation results are encouraging
and demonstrate the method's efficiency and practicality.
