Abstract
This paper presents various Metamodel–Assisted Evolution Strategies which reduce the computational cost of optimisation problems involving time—consuming function evaluations. The metamodel is built using previously evaluated solutions in the search space and utilized to predict the fitness of new candidate solutions. In addition to previous works by the authors, the new metamodel takes also into account the error associated with each prediction, by correlating neighboring points in the search space. A mathematical problem and the problem of designing an optimal airfoil shape under viscous flow considerations have been worked out. Both demonstrate the noticeable gain in computational time one might expect from the use of metamodels in Evolution Strategies.
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Emmerich, M., Giotis, A., Özdemir, M., Bäck, T., Giannakoglou, K. (2002). Metamodel—Assisted Evolution Strategies. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_35
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DOI: https://doi.org/10.1007/3-540-45712-7_35
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