We look forward to trying several other ideas for addressing the issue of expensive fitness evaluation. With an expensive evaluation function we can afford to spend more time learning from experience than would normally be the case with evolutionary algorithms, because the additional time required may be small compared to the time spent in fitness evaluation. Even running machine learning algorithms to tune parameters and identify effective operators on the fly might be feasible for problems with very expensive fitness evaluation.