Abstract
Traditional evolutionary computations use random sampling strategy to gene rate their first generation resulting in very few (if any) good solutions found in the first generation. Over-sampling is a strategy of the deliberate selection of individuals of a rare type in order to obtain reasonably precise estimates of the properties of this type. We believe that the use of over-sampling in generating the first generation of IEC would result in better performance. We proposed two types of over-sampling process in IEC (OIEC_1 and OIEC_2), and used mineral water bottle design as a research case to verify the proposed models’ performance. The initial results shown that both proposed models performed better than traditional IEC.
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Hung, MH., Hsu, FC. (2005). Accelerating Interactive Evolutionary Computation Convergence Pace by Using Over-sampling Strategy. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_71
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DOI: https://doi.org/10.1007/3-540-32391-0_71
Publisher Name: Springer, Berlin, Heidelberg
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