Estimation　of　distribution　algorithms　(EDAs)　are　a　special　class　of　model-based　evolutionary　algorithms　(EAs).　To　improve　the　performance　of　traditional　EDAs,　many　remedies　were　suggested,　which　mainly　focused　on　estimating　a　suitable　probability　distribution　model　with　superior　solutions.　Different　from　existing　research　ideas,　this　paper　tries　to　enhance　EDA　by　exploiting　the　potential　value　of　inferior　solutions,　where　Gaussian　EDA　is　taken　as　an　example.　It　will　be　shown　that,　after　a　simple　repair　operation,　inferior　solutions　could　be　surprisingly　useful　in　adjusting　the　covariance　matrix　of　Gaussian　model,　then　a　better　search　direction　and　a　more　proper　search　scale　can　be　obtained.　Since　the　aim　of　Inferior　Solution　Repairing　(ISR)　operator　is　not　to　directly　improve　the　quality　of　inferior　solutions,　but　to　make　them　closer　to　superior　ones,　it　can　be　implemented　in　a　simple　way.　Combining　ISR　and　traditional　Gaussian　EDA,　a　new　EDA　variant　named　ISR-EDA　is　developed.　Comparison　with　existing　EDAs　and　some　other　state-of-the-art　EAs　on　benchmark　functions　demonstrates　that　ISR-EDA　is　efficient　and　competitive.