Using Singular Value Decomposition to Improve a Genetic Algorithm’s Performance

The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of Genetic Algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way that provides both a saving in storage space and an improvement in information retrieval. It will be shown that these beneficial properties can be extended to many other different types of comparison as well. Briefly, vectors representing the genes of individuals are projected into a new low-dimensional space, obtained by the singular value decomposition of a gene-individual matrix. The information about what it means to be a good or bad individual serves as a basis for qualifying candidate individuals for reinsertion into the next generation. Positive results from different approaches of this application are presented and evaluated. In addition, several possible alternative techniques are proposed and considered.