Abstract
This paper introduces a new robust optimization technique which performs tolerance and parameter design using a genetic algorithm. It is demonstrated how tolerances for control parameters can be specified while reducing the product’s sensitivity to noise factors. As generations of solutions undergo standard genetic operations, new designs evolve, which exhibit several important characteristics. First, all control parameters in an evolved design are within a set of allowed tolerances; second, the resulting product response meets the target performance; and finally, the product response variance is minimal.
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© 2004 Springer-Verlag Berlin Heidelberg
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Forouraghi, B. (2004). Robust Engineering Design with Genetic Algorithms. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_58
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DOI: https://doi.org/10.1007/978-3-540-24677-0_58
Publisher Name: Springer, Berlin, Heidelberg
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