Swarm　intelligence　in　a　bat　algorithm　(BA)　provides　social　learning.　Genetic　operations　for　reproducing　individuals　in　a　genetic　algorithm　(GA)　offer　global　search　ability　in　solving　complex　optimization　problems.　Their　integration　provides　an　opportunity　for　improved　search　performance.　However,　existing　studies　adopt　only　one　genetic　operation　of　GA,　or　design　hybrid　algorithms　that　divide　the　overall　population　into　multiple　subpopulations　that　evolve　in　parallel　with　limited　interactions　only.　Differing　from　them,　this　work　proposes　an　improved　self-adaptive　bat　algorithm　with　genetic　operations　(SBAGO)　where　GA　and　BA　are　combined　in　a　highly　integrated　way.　Specifically,　SBAGO　performs　their　genetic　operations　of　GA　on　previous　search　information　of　BA　solutions　to　produce　new　exemplars　that　are　of　high-diversity　and　high-quality.　Guided　by　these　exemplars,　SBAGO　improves　both　BA＇s　efficiency　and　global　search　capability.　We　evaluate　this　approach　by　using　29　widely-adopted　problems　from　four　test　suites.　SBAGO　is　also　evaluated　by　a　real-life　optimization　problem　in　mobile　edge　computing　systems.　Experimental　results　show　that　SBAGO　outperforms　its　widely-used　and　recently　proposed　peers　in　terms　of　effectiveness,　search　accuracy,　local　optima　avoidance,　and　robustness.