%0 Book Section %1 noKey %A Deb, Kalyanmoy %A Srinivasan, Aravind %B Multiobjective Problem Solving from Nature %D 2008 %E Knowles, Joshua %E Corne, David %E Deb, Kalyanmoy %E Chair, DevaRaj %I Springer Berlin Heidelberg %K design discovery innovative innovization masterbib %P 243-262 %R 10.1007/978-3-540-72964-8_12 %T Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization %U http://dx.doi.org/10.1007/978-3-540-72964-8_12 %X In optimization studies, often researchers are interested in finding one or more optimal or near-optimal solutions. In this chapter, we describe a systematic optimization-cum-analysis procedure which performs a task beyond simply finding optimal solutions, but first finds a set of near-Pareto-optimal solutions and then analyses them to unveil salient knowledge about properties which make a solution optimal. The proposed ‘innovization’ task is explained and its working procedure is illustrated on a number of engineering design tasks. The variety of problems chosen in the chapter and the resulting innovations obtained for each problem amply demonstrate the usefulness of the proposed innovization task. The procedure is a by-product of performing a routine multiobjective optimization for a design task and in our opinion portrays an important process of knowledge discovery which may not be possible to achieve by other means. %@ 978-3-540-72963-1

@incollection{noKey, abstract = {In optimization studies, often researchers are interested in finding one or more optimal or near-optimal solutions. In this chapter, we describe a systematic optimization-cum-analysis procedure which performs a task beyond simply finding optimal solutions, but first finds a set of near-Pareto-optimal solutions and then analyses them to unveil salient knowledge about properties which make a solution optimal. The proposed ‘innovization’ task is explained and its working procedure is illustrated on a number of engineering design tasks. The variety of problems chosen in the chapter and the resulting innovations obtained for each problem amply demonstrate the usefulness of the proposed innovization task. The procedure is a by-product of performing a routine multiobjective optimization for a design task and in our opinion portrays an important process of knowledge discovery which may not be possible to achieve by other means.}, added-at = {2014-06-11T18:59:47.000+0200}, author = {Deb, Kalyanmoy and Srinivasan, Aravind}, biburl = {https://www.bibsonomy.org/bibtex/235605eb0d880cd4c28eeae273150c0b0/marcioweck}, booktitle = {Multiobjective Problem Solving from Nature}, description = {Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization - Springer}, doi = {10.1007/978-3-540-72964-8_12}, editor = {Knowles, Joshua and Corne, David and Deb, Kalyanmoy and Chair, DevaRaj}, interhash = {9d8eca45749f5598339b16b348d7ffcd}, intrahash = {35605eb0d880cd4c28eeae273150c0b0}, isbn = {978-3-540-72963-1}, keywords = {design discovery innovative innovization masterbib}, pages = {243-262}, publisher = {Springer Berlin Heidelberg}, series = {Natural Computing Series}, timestamp = {2014-06-11T18:59:47.000+0200}, title = {Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization}, url = {http://dx.doi.org/10.1007/978-3-540-72964-8_12}, year = 2008 }