An　improved　genetic　algorithm　is　proposed　for　solving　premature　convergence.　Firstly,　the　population　is　divided　into　several　sub-populations　by　the　minimum　spanning　tree　clustering.　Then,　the　genetic　operation　is　performed　among　individuals　within　sub-population　which　ensures　the　evolution　direction　and　speed,　and　that　among　individuals　between　different　sub-populations　which　provides　diversity　by　avoiding　inbreeding.　The　experimental　results　on　23　benchmark　functions　using　binary　and　real-valued　representations　show　that　the　proposed　algorithm　has　better　convergence　and　faster　speed　to　get　the　optimal　solution.　©　2016,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.