Visualized IEC: interactive evolutionary computation with multidimensional data visualization

We propose Visualized IEC as an interactive evolutionary computation (IEC) with visualizing individuals in a multidimensional searching space in a 2D space. This visualization helps us envision the landscape of an n-D searching space; so that it is easier for us to join an EC search, by indicating the possible global optimum estimated in the 2D mapped space. We experimentally evaluate the effect of visualization using a benchmark function. We use self-organizing maps for this projection of individuals onto a 2D space. The experimental result shows that the convergence speed of the GA with human search on the visualized space is at least five times faster than a conventional GA.