Feature selection for problem decomposition on high dimensional optimization

In general, the Cooperative Coevolutionary Algorithms based on separability have shown good performance when solving high dimensional optimization problems. However, the number of function evaluations required for the decomposition stage of these algorithms can growth very fast, and depends on the dimensionality of the problem. In cases where a single function evaluation is computationally expensive or time consuming, it is of special interest keeping the function evaluations as low as possible. In this document we propose the use of a feature selection technique for choosing the most important decision variables of an optimization problem in order to apply separability analysis on a reduced decision variable set intending to save the most optimization resources.