This　article　proposes　an　advanced　cognition-driven　electromagnetic　(EM)　optimization　incorporating　transfer　function-based　feature　surrogate　for　EM　optimization　of　microwave　filters.　The　proposed　optimization　technique　addresses　the　situations　where　the　response　of　the　starting　point　for　design　optimization　is　far　away　from　the　design　specifications.　This　article　proposes　to　extract　transfer　function-based　feature　parameters　for　optimization　to　address　the　challenge　that　the　features　cannot　be　clearly　and　explicitly　identified　from　the　filter　response.　Multiple　transfer　function-based　feature　parameters　are　extracted　and　used　to　develop　the　feature　surrogate　model　for　the　proposed　cognition-driven　optimization.　Furthermore,　we　derive　new　objective　functions　for　the　cognition-driven　optimization　directly　in　the　feature　space.　The　proposed　cognition-driven　optimization　incorporating　transfer　function-based　feature　surrogate　can　achieve　faster　convergence　than　the　existing　feature-assisted　EM　optimization　methods.　Two　examples　of　EM　optimizations　of　microwave　filters　are　used　to　demonstrate　the　proposed　technique.