(1) the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result, Deb, K. and Srinivasan, A. (in press). Innovization: Innovating design principles through optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), to be held in Seattle, USA during 8-12 July 2006. (2) the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper, Kalyanmoy Deb Professor Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur. PIN 208016, India Email: deb@iitk.ac.in Tel: 0091 512 259 7205 (office), 8310 (home) Aravind Srinivasan Graduate Student Department of Mechanical Engineering Indian Institute of Technology Kanpur Kanpur. PIN 208016, India Email: aravinds@iitk.ac.in Tel: 0091 512 259 7668 (office) (3) the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition), Kalyanmoy Deb (deb@iitk.ac.in) (4) the abstract of the paper(s), Designers and practitioners routinely look for new and improved solutions and often resort to forming and solving an optimization problem in order to find the best solution corresponding to a chosen objective or a design goal. Although such a single optimum solution may bring out some innovation in its design or working principle, often they could be very specific to the chosen objective. In this paper, we suggest a multi-objective optimization strategy which is capable of finding more than one optimal solution, each corresponding to a certain trade-off among the objectives. Thereafter, we suggest a systematic information retrieval strategy to decipher salient design principles which are common to these multiple optimal solutions. Such a dual task (we call it an 'innovization' task) is demonstrated to discover useful and importantly, innovative, relationships among objectives and decision variables which must be present in solution to make it a high-performing, optimal solution. Such valuable information about a design problem are difficult to achieve by any other scientific method. In addition to simply finding an optimal solution, the innovization task allows one to reveal important 'recipes' and 'blue-prints' for a solving a problem optimally, which are not often intuitive and beyond human comprehension. In a number of engineering component design problems, we discover useful relationships, such as a fixed thickness and a fixed difference in inner and outer diameters of a multi-disk brake system are properties of all optimal brakes ranging from small to large stopping-time considerations (with a trade-off with brake mass). Similarly, in a spring design problem, small to large sized (having large to small developed stress) optimal springs having different wire diameters, spring diameters, and number of turns, all having a specific spring stiffness qualify to be the optimal designs. Such information are not often human-conceivable and intuitive. The proposed innovization procedure allows a way to unveil such useful design principles common to multiple optimal solutions. (5) a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies, We feel that our work falls into the following two categories: D) The result is publishable in its own right as a new scientific result - independent of the fact that the result was mechanically created. (G) The result solves a problem of indisputable difficulty in its field. (6) a statement stating why the result satisfies the criteria that the contestant claims (see the examples below as a guide to aid in constructing this part of the submission), We now argue why we think our entry satisfies each of the above two categories. D) The innovization principles obtained by the post-optimality analysis are mathematical relationships among decision variables and objectives, which are derived from multiple optima. In the context of an electric motor design problem, the deciphered innovization principles may translate to the following concept. An optimal procedure of designing a motor for increased power output is to proportionately increase the armature diameter and have a fixed wire diameter. After such relationships are deciphered from the multiple trade-off optimal solutions (say, simultaneous maximization of power output and minimization of size of motor, in the above example), they can be substituted in the original optimization problem formulation to simply the problem and optimality of the relationships can be tested using mathematical optimization principles. Since the relationships are properties of trade-off *optimal* solutions, they are difficult to find by other means, except by first finding a representative set of optimal solutions and then looking for interesting relationships among them. Thus, although the results are mechanically created by using an evolutionary multi-objective optimization procedure followed by a number of local searches and other multi-objective optimization aids to increase the confidence in the optimality of obtained solutions, the information retrieval strategy performed by authors in specific engineering design problems (the idea is also followed by other researchers) has always discovered innovative and useful design principles which were publishable in domain specific journals. G) As mentioned above, the innovization task is also a unique procedure of obtaining innovative design principles in a problem. The innovization idea is new and systematic, and there does not exist any known methodology for achieving a similar task. It is worth mentioning here that the 'Monotonicity Analysis' of identifying optimal solutions without actually performing an optimization task can find some such useful relationships among decision variables, but the technique is only applicable to monotonic objective functions and constraints and is not at all a generic approach. (7) a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Deb, K. and Srinivasan, A. (in press). Innovization: Innovating design principles through optimization. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), to be held in Seattle, USA during 8-12 July 2006. (8) a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; and 80% (Deb) - 20% (Aravind) (9) a statement stating why the judges should consider the entry as "best" in comparison to other entries that may also be "human-competitive." The idea proposed in our entry is a level higher than probably the most human-competitive results proposed so far. It is likely the most other results are based on a single optimal solution corresponding to minimizing or maximizing a specific objective function related to the problem. Our innovization procedure would allow multiple conflicting objectives to be considered, thereby allowing to find a number of optimal solutions, trading-off among conflicting objectives. Moreover, instead of analyzing human competitiveness and innovation associated with a single optimal solution, our innovization procedure brings out common principles associated with a number of such trade-off yet optimal solutions (including the single-objective optimum solution), which would remain as higher-level features of human competitiveness and innovation. In this entry, we suggest a systematic procedure of performing this task and demonstrate its usage on a number of engineering design case studies. In each case, previously unknown yet interesting human competitive design principles were unveiled. In some cases, human conceivable results are also found. The most unique aspect of our entry is that the proposed innovization technique can be used to *learn* about salient properties of optimal (high-performing) solutions, trading-off two or more goals of design. To put it in the context of a concrete example, the proposed technique is way to unveil how different motoring parameters, such as coil diameter, number of turns, armature diameter, etc., must be varied with respect to each other in designing minimum-sized motors with an increasing delivered power. The idea goes beyond simply finding an optimal design, but trying to understand how to make and what makes a design optimal.