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Design informatics, which is the efficient design methodology, has three points of view. The first is the efficient exploration in design space using evolutionary-based optimization methods. The second is the structuring and visualizing of design space using data mining techniques. The third is the application to practical problems. In the present study, the influence of the difference among the seven pure and hybrid optimization methods for design information has been investigated in order to explain the selection manner of optimization methods for data mining. The practical problem of a singlestage hybrid rocket is picked up as the present design object. A functional analysis of variance and a self-organizing map are employed as data mining techniques in order to acquire the global information in design space. As a result, mining result depends on not the number of generation (i.e. convergence) but the optimization methods (i.e. diversity). Consequently, the optimization method with diversity performance is the beneficial selection in order to obtain the global design information in design space.

Influence of Difference among Evolutionary Computations for Design Information