Nonlinear　complex　system　modeling　has　drawn　attention　from　diverse　fields　and　many　approaches　have　been　developed.　Among　those　approaches,　the　advantages　of　the　belief　rule　base　(BRB)　expert　system　have　been　shown　for　managing　multiple　types　of　information　under　uncertainty　and　modeling　the　nonlinearity　present　in　many　theoretical　and　practical　complex　systems.　However,　two　challenges　still　need　to　be　addressed.　First,　BRB　needs　to　be　downsized　to　conserve　modeling　and　computational　effort.　For　this　challenge,　a　new　disjunctive　assumption　is　applied,　which　can　significantly　downsize　BRB　while　maintaining　its　completeness.　Second,　the　structure　and　parameters　of　BRB　need　to　be　jointly　optimized.　For　this　challenge,　a　new　Akaike　information　criterion　(AIC)-based　optimization　objective　is　derived　to　represent　both　modeling　accuracy　and　modeling　complexity.　Moreover,　a　joint　bi-level　optimization　model　with　an　AIC-based　objective　is　constructed　for　the　BRB　structure　and　parameters,　and　a　bi-level　optimization　algorithm　is　proposed.　Three　evolutionary　algorithms,　namely,　the　genetic　algorithm,　particle　swarm　optimization　algorithm,　and　differential　evolutionary　algorithm,　are　tested　in　a　comparative　fashion　to　determine　the　best　fit　for　the　optimization　engine.　The　results　of　two　practical　case　studies　show　that　the　joint　optimization　approach　can　identify　an　optimal　configuration　for　both　its　structure　and　parameters,　which　is　referred　to　as　the　best　decision　structure　in　this　paper.