This paper addresses the problem of determining the next set of releases in the course of software evolution. It formulates both ranking and selection of candidate software components as a series of feature subset selection problems to which search based software engineering can be applied. The approach is automated using greedy and simulated annealing algorithms and evaluated using a set of software components from the component base of a large telecommunications organisation. The results are compared to those obtained by a panel of (human) experts. The results show that the two automated approaches convincingly outperform the expert judgment approach.