Incorporating user preferences into evolutionary multi-ob- jective evolutionary algorithms has been an important topic in recent research in the area of evolutionary multi-objective optimization. We present a very simple and yet very effective modification to the Approx- imation-Guided Evolution (AGE) algorithm to incorporate user prefer- ences. Over a wide range of test functions, we observed that the resulting algorithm called iAGE is just as good at finding evenly distributed so- lutions as similarly modified NSGA-II and SPEA2 variants. However, in particular for ”difficult” two-objective problems and for all three- objective problems we see more evenly distributed solutions in the user preferred region when using iAGE.