?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=https%3A%2F%2Fpure.iiasa.ac.at%2Fid%2Feprint%2F4546%2F&rft.title=Solving+Multiple+Objective+Programming+Problems+Using+Feed-Forward+Artificial+Neural+Networks%3A+The+Interactive+FFANN+Procedure&rft.creator=Sun%2C+M.&rft.creator=Stam%2C+A.&rft.creator=Steuer%2C+R.E.&rft.description=In+this+paper%2C+we+propose+a+new+interactive+procedure+for+solving+multiple+objective+programming+problems.+Based+upon+feed-forward+artificial+neural+networks+(FFANNs)%2C+the+method+is+called+the+Interactive+FFANN+Procedure.+In+the+procedure%2C+the+decision+maker+articulates+preference+information+over+representative+samples+from+the+nondominated+set+either+by+assigning+preference+%22values%22+to+the+sample+solutions+or+by+making+pairwise+comparisons+in+a+fashion+similar+to+that+in+the+Analytic+Hierarchy+Process.+With+this+information%2C+a+FFANN+is+trained+to+represent+the+decision+maker's+preference+structure.+Then%2C+using+the+FFANN%2C+an+optimization+problem+is+solved+to+search+for+improved+solutions.+An+example+is+given+to+illustrate+the+Interactive+FFANN+Procedure.+Also%2C+the+procedure+is+compared+computationally+with+the+Tchebycheff+Method+(Steuer+and+Choo+1983).+From+the+computational+results%2C+the+Interactive+FFANN+Procedure+produces+good+results+and+is+robust+with+regard+to+the+neural+network+architecture.&rft.publisher=WP-95-046&rft.date=1995-05&rft.type=Monograph&rft.type=NonPeerReviewed&rft.format=text&rft.language=en&rft.identifier=https%3A%2F%2Fpure.iiasa.ac.at%2Fid%2Feprint%2F4546%2F1%2FWP-95-046.pdf&rft.identifier=++Sun%2C+M.%2C+Stam%2C+A.+%3Chttps%3A%2F%2Fpure.iiasa.ac.at%2Fview%2Fiiasa%2F1588.html%3E%2C+%26+Steuer%2C+R.E.++(1995).++Solving+Multiple+Objective+Programming+Problems+Using+Feed-Forward+Artificial+Neural+Networks%3A+The+Interactive+FFANN+Procedure.+++IIASA+Working+Paper.+IIASA%2C+Laxenburg%2C+Austria%3A+WP-95-046+++++