Scheduling variance loss using population level annealing for evolutionary computation

Evolutionary programming (EP) has historically used a number of approaches for selection of the mutation step size. Current EP implementations typically use self-adaptive meta-parameters for mutation step size selection. However, one of the potential drawbacks of this scheme is that it is not directly responsive to the variance reduction caused by selection. We investigate an alternate method for mutative step size selection that reacts directly to the variance-reducing effects of selection.