Due　to　the　insufficiency　of　utilizing　knowledge　to　guide　the　complex　optimal　searching,　existing　genetic　algorithms　fail　to　effectively　solve　excavator　boom　structural　optimization　problem.　To　improve　the　optimization　efficiency　and　quality,　a　new　knowledge-based　real-coded　genetic　algorithm　is　proposed.　A　dual　evolution　mechanism　combining　knowledge　evolution　with　genetic　algorithm　is　established　to　extract,　handle　and　utilize　the　shallow　and　deep　implicit　constraint　knowledge　to　guide　the　optimal　searching　of　genetic　algorithm　circularly.　Based　on　this　dual　evolution　mechanism,　knowledge　evolution　and　population　evolution　can　be　connected　by　knowledge　influence　operators　to　improve　the　configurability　of　knowledge　and　genetic　operators.　Then,　the　new　knowledge-based　selection　operator,　crossover　operator　and　mutation　operator　are　proposed　to　integrate　the　optimal　process　knowledge　and　domain　culture　to　guide　the　excavator　boom　structural　optimization.　Eight　kinds　of　testing　algorithms,　which　include　different　genetic　operators,　are　taken　as　examples　to　solve　the　structural　optimization　of　a　medium-sized　excavator　boom.　By　comparing　the　results　of　optimization,　it　is　shown　that　the　algorithm　including　all　the　new　knowledge-based　genetic　operators　can　more　remarkably　improve　the　evolutionary　rate　and　searching　ability　than　other　testing　algorithms,　which　demonstrates　the　effectiveness　of　knowledge　for　guiding　optimal　searching.　The　proposed　knowledge-based　genetic　algorithm　by　combining　multi-level　knowledge　evolution　with　numerical　optimization　provides　a　new　effective　method　for　solving　the　complex　engineering　optimization　problem.