This　paper　presents　a　novel　genetic　algorithm　(GA)　for　multiple　sequence　alignment　in　protein　analysis.　The　most　significant　improvement　afforded　by　this　algorithm　results　from　its　use　of　segment　profiles　to　generate　the　diversified　initial　population　and　prevent　the　destruction　of　conserved　regions　by　crossover　and　mutation　operations.　Segment　profiles　contain　rich　local　information,　thereby　speeding　up　convergence.　Secondly,　it　introduces　the　use　of　the　norMD　function　in　a　genetic　algorithm　to　measure　multiple　alignment　Finally,　as　an　approach　to　the　premature　problem,　an　improved　progressive　method　is　used　to　optimize　the　highest-scoring　individual　of　each　new　generation.　The　new　algorithm　is　compared　with　the　ClustalX　and　T-Coffee　programs　on　several　data　cases　from　the　BAliBASE　benchmark　alignment　database.　The　experimental　results　show　that　it　can　yield　better　performance　on　data　sets　with　long　sequences,　regardless　of　similarity.　©　Springer-Verlag　Berlin　Heidelberg　2006.