A comparison of differential evolution and genetic algorithms for the column subset selection problem

Abstract

The column subset selection problem is a well-known complex optimization problem that has a number of appealing real-world applications including network and data sampling, dimension reduction, and feature selection. There are a number of traditional deterministic and randomized heuristic algorithms for this problem. Recently, it has been tackled by a variety of bio-inspired and evolutionary methods. In this work, differential evolution, a popular and successful real-parameter optimization algorithm, adapted for fixed-length subset selection, is used to find solutions to the column subset selection problem. Its results are compared to a recent genetic algorithm designed for the same purpose.