Comparative studies of sets of control parameter values are commonly performed when tuning an evolutionary algorithm for a class of problem instances. The standard approach is to identify the most useful set of control parameter settings for a domain. In this paper, we propose an alternative anytime algorithm portfolio technique in which computational resources are allocated among multiple sets of control parameter value settings. We show a method of optimizing such portfolios by applying a bootstrap sampling approach to a database of individual algorithm performance on instances from a problem distribution. Experiments with genetic algorithms applied to the traveling salesperson domain show that the portfolio approach can yield better performance on a distribution of problem instances than the standard approach of trying to identify the single best configuration for the problem class.