In　this　paper,　we　study　a　variant　of　the　orienteering　problem　called　the　clustered　orienteering　problem.　In　this　problem,　customers　are　grouped　into　clusters.　A　profit　is　associated　with　each　cluster　and　is　collected　if　and　only　if　all　customers　in　the　cluster　are　served.　A　single　vehicle　is　available　to　visit　the　customers.　The　goal　is　to　maximize　the　total　profits　collected　within　a　maximum　travel　time　limit.　To　address　this　NP-hard　problem,　we　propose　the　first　evolutionary　algorithm　that　integrates　a　backbone-based　crossover　operator　and　a　destroy-and-repair　mutation　operator　for　search　diversification　and　a　solution-based　tabu　search　procedure　reinforced　by　a　reinforcement　learning　mechanism　for　search　intensification.　The　experiment　results　indicate　that　our　algorithm　outperforms　the　state-of-the-art　algorithms　from　the　literature　on　a　wide　range　of　924　well-known　benchmark　instances.　In　particular,　the　proposed　algorithm　obtains　new　records　(new　lower　bounds)　for　14　instances　and　finds　the　best-known　solutions　for　the　remaining　instances.　Furthermore,　a　new　set　of　72　large　instances　with　50　to　100　clusters　and　at　least　400　vertices　is　generated　to　evaluate　the　scalability　of　the　proposed　algorithm.　Results　show　that　the　proposed　algorithm　manages　to　outperform　three　state-of-the-art　COP　algorithms.　We　also　adopt　our　algorithm　to　solve　a　dynamic　version　of　the　COP　considering　stochastic　travel　time.(c)　2023　Elsevier　B.V.　All　rights　reserved.