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Information Journal Paper

Title

OPTIMIZING MULTI-RESPONSE STATISTICAL PROBLEMS USING A GENETIC ALGORITHM

Pages

  50-59

Keywords

Not Registered.

Abstract

 In this paper, two methods to solve multi-response statistical problems are presented. In these methods, desirability function, genetic algorithm and simulation methodology are applied. The desirability function is responsible for modeling the multi-response statistical problem, the genetic algorithm tries to optimize the model and, finally, the simulation approach generates the required input data from a simulated system. The methods differ from each other in controlling the randomness of the problem. In the first method, replications control this randomness, while, in the second method, the randomness is controlled by a statistical test. Furthermore, these methods are compared by designed experiments and the results are reported.  

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    • Cite

    APA: Copy

    PASANDIDEH, S.H.R., & AKHAVAN NIAKI, S.T.. (2006). OPTIMIZING MULTI-RESPONSE STATISTICAL PROBLEMS USING A GENETIC ALGORITHM. SCIENTIA IRANICA, 13(1), 50-59. SID. https://sid.ir/paper/289317/en

    Vancouver: Copy

    PASANDIDEH S.H.R., AKHAVAN NIAKI S.T.. OPTIMIZING MULTI-RESPONSE STATISTICAL PROBLEMS USING A GENETIC ALGORITHM. SCIENTIA IRANICA[Internet]. 2006;13(1):50-59. Available from: https://sid.ir/paper/289317/en

    IEEE: Copy

    S.H.R. PASANDIDEH, and S.T. AKHAVAN NIAKI, “OPTIMIZING MULTI-RESPONSE STATISTICAL PROBLEMS USING A GENETIC ALGORITHM,” SCIENTIA IRANICA, vol. 13, no. 1, pp. 50–59, 2006, [Online]. Available: https://sid.ir/paper/289317/en

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