We propose Visualized IEC as an interactive evolutionary computation (IEC) with visualizing individuals in a multidimensional searching space in a 2-D 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 2-D mapped space. We experimentally evaluate the effect of visualization using a benchmark function. We use self-organizing maps for the projection of individuals onto a 2-D space. The experimental result shows that the convergence speed of GA with human search on the visualized space is at least five times faster than a conventional GA.
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