Human-based genetic algorithms (HBGA) use both human evaluation and innovation to optimize a population of solutions (Kosorukoff, 2001). The novel contribution of HBGAs is an introduction of human-based innovation operators. However, there was no attempt to measure the effect of human-based innovation operators on the overall performance of GAs quantitatively, in particular, by comparing the performance of HBGAs and interactive genetic algorithms (IGA) that do not use human innovation. This paper shows that the mentioned effect is measurable and further focuses on quantitative comparison of the efficiency of these two classes of algorithms. In order to achieve this purpose, this paper proposes an interactive analog of the one-max problem, suggests human-based innovation operators appropriate for this problem, and compares convergence results of HBGA and IGA for the same problem.