Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Benchmarking and solving dynamic constrained problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Closed-loop evolutionary multiobjective optimization
IEEE Computational Intelligence Magazine
Policy learning in resource-constrained optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On handling ephemeral resource constraints in evolutionary search
Evolutionary Computation
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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.