From　the　aspect　of　solution　space,　the　role　of　crossover　operators　is　analyzed.　For　the　disadvantage　of　aimless　search,　an　improved　genetic　algorithm　based　on　oriented　crossover　is　proposed,　which　can　make　the　offspring　individuals　evolve　towards　the　target　value　by　optimizing　their　crossover　positions.　The　evolving　probability　is　very　large.　The　simulation　results　show　that　the　algorithm　can　improve　greatly　the　efficiency　and　precision　to　find　the　optimum　value.