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  2. Test functions for optimization - Wikipedia

    en.wikipedia.org/wiki/Test_functions_for...

    The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and Binh. [6] The software developed by Deb can be downloaded, [ 7 ] which implements the NSGA-II procedure with GAs, or the program posted on Internet, [ 8 ] which implements the NSGA-II procedure with ES.

  3. Testsigma - Wikipedia

    en.wikipedia.org/wiki/Testsigma

    Testsigma has multiple products to let software testing teams test web apps, mobile apps, APIs and ERP applications like Salesforce. [ 8 ] [ 9 ] Testsigma claims Nagra , Samsung , Cisco , Bosch , NTUC Fairprice as customers.

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  5. Process corners - Wikipedia

    en.wikipedia.org/wiki/Process_corners

    In semiconductor manufacturing, a process corner is an example of a design-of-experiments (DoE) technique that refers to a variation of fabrication parameters used in applying an integrated circuit design to a semiconductor wafer. Process corners represent the extremes of these parameter variations within which a circuit that has been etched ...

  6. Design for Six Sigma - Wikipedia

    en.wikipedia.org/wiki/Design_for_Six_Sigma

    The methodology explains how to build predictive statistical models for software reliability and robustness and shows how simulation and analysis techniques can be combined with structural design and architecture methods to effectively produce software and information systems at Six Sigma levels.

  7. Simulation-based optimization - Wikipedia

    en.wikipedia.org/wiki/Simulation-based_optimization

    For example, there might be too many possible values for input variables, or the simulation model might be too complicated and expensive to run for a large set of input variable values. In these cases, the goal is to iterative find optimal values for the input variables rather than trying all possible values.