Evolutionary hybrid computation in view of design information by data mining

Design Informatics has three points of view. First point is the efficient exploration in design space using evolutionary computation. Second point is the structurization and visualization of design space using data mining. Third point is the application to practical problems. In the present study, the influence of the seven pure and hybrid optimizers for design information has been investigated in order to explain the selection manner of optimizer for data mining. A single-stage hybrid rocket design problem is picked up as the present design object. As a result, mining result depends on not the number of generation (convergence) but the optimizers (diversity). Consequently, the optimizer with diversity performance should be selected in order to obtain global design information in the design space. Therefore, the diversity performance has also been explained for the seven optimization methods by using three standard mathematical test problems with/without noise. The result indicates that the hybrid method between the differential evolution and the genetic algorithm is beneficial performance for efficient exploration in the design space under the condition for large-scale design problems within 102 order evolution at most. Moreover, the comparison among eight crossovers indicates that the principal component analysis blended crossover is good selection on the hybrid method between the differential evolution and the genetic algorithm.