In　traditional　methods,　features　extracted　from　the　hysteresis　loop　are　manually　analyzed　to　establish　a　model　for　predicting　tensile　force　in　one　ferromagnetic　material.　However,　the　hysteresis　loop　is　sensitive　to　material　type,　thus　increasing　the　difficulty　in　predicting　tensile　forces　in　two　materials.　Moreover,　manual　feature　extraction　is　time　consuming.　It　is　necessary　to　complete　automatic　feature　extraction　and　elimination　of　the　influence　of　material　type　on　the　hysteresis　loop　in　order　to　automatically　establish　a　prediction　model　for　tensile　force　prediction　in　two　materials.　In　this　article,　an　evolutionary　computation-based　method　was　proposed　to　perform　the　above-mentioned　two　tasks　simultaneously.　The　proposed　method　involves　three　steps.　First,　hysteresis　loops　of　two　materials　under　different　tensile　forces　were　measured.　Second,　the　measured　hysteresis　loops　were　transformed　into　corresponding　transformed　ascending　hysteresis　curves　through　a　transformation　operation　of　the　curve　of　hysteresis　loop　change　(CHLC)　for　eliminating　the　influence　of　material　type　on　the　hysteresis　loop.　Third,　an　improved　bat　algorithm　was　proposed　to　eliminate　further　the　influence　of　material　type　on　the　transformed　ascending　hysteresis　curve.　The　improved　bat　algorithm　iteratively　gave　the　position　of　a　material　type-insensitive　feature　on　the　transformed　ascending　hysteresis　curve.　The　material　type-insensitive　feature-based　neural　network　was　used　to　predict　tensile　forces　in　two　materials.　Experimental　results　verified　the　proposed　method.　The　motivation　of　obtaining　a　general　tensile　force　prediction　model,　the　applicability　of　the　proposed　method　in　more　than　two　materials,　and　the　influence　of　CHLC　transformation　operation　on　the　improved　bat　algorithm　and　neural　network　are　also　discussed.