Abstract
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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References
Klockgether, J., Schwefel, H.-P.: Two-Phase Nozzle and Hollow Core Jet Experiments. Engineering Aspects of Magnetohydrodynamics, 141–148 (1970)
Rechenberg, I.: Case studies in evolutionary experimentation and computation. Computer Methods in Applied Mechanics and Engineering 186, 125–140 (2000)
Allmendinger, R., Knowles, J.: Ephemeral resource constraints in optimization and their effects on evolutionary search. Technical report MLO-20042010, University of Manchester (2010), http://www.cs.man.ac.uk/~allmendr/publications.html
Bosman, P.A.N., La Poutré, H.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: Proc. of GECCO, pp. 1165–1172 (2007)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Dordrecht (2001)
Nguyen, T.T., Yao, X.: Benchmarking and Solving Dynamic Constrained Problems. In: Proc. of IEEE CEC, pp. 690–697 (2009)
Knowles, J.: Closed-Loop Evolutionary Multiobjective Optimization. IEEE Computational Intelligence Magazine 4(3), 77–91 (2009)
King, R.D., Whelan, K.E., Jones, F.M., Reiser, P.G.K., Bryant, C.H., Muggleton, S.H., Kell, D.B., Oliver, S.G.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Shir, O.M., Emmerich, M., Bäck, T., Vrakking, M.J.J.: The application of evolutionary multi-criteria optimization to dynamic molecular alignment. In: Proc. of IEEE ICEC, pp. 4108–4115 (2007)
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Allmendinger, R., Knowles, J. (2010). On-Line Purchasing Strategies for an Evolutionary Algorithm Performing Resource-Constrained Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_17
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DOI: https://doi.org/10.1007/978-3-642-15871-1_17
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