We consider an optimization scenario in which resources are required in order to realize or evaluate candidate solutions. The particular resources required are a function of the solution vectors, and moreover, resources are costly, can be stored only in limited supply, and have a shelf life. Since it is not convenient or realistic to arrange for all resources to be available at all times, resources must be purchased on-line in conjunction with the working of the optimizer, here an evolutionary algorithm (EA). We devise three resource-purchasing strategies (for use in an elitist generational EA), and deploy and test them over a number of resource-constraint settings. We find that a just-in-time method is generally effective, but a sliding-window approach is better in the presence of a small budget and little storage space.
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