Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Pattern identification in pareto-set approximations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Hybrid search for faster production and safer process conditions in friction stir welding
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Automated innovization for simultaneous discovery of multiple rules in bi-objective problems
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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This work concerns the post-optimal analysis of the trade-off front of a multi-objective optimization problem to extract useful design knowledge pertaining to these high-performing solutions. The expected knowledge basically consists of statistically significant relationships between the objective functions and decision variables. These relationships are represented in an intuitive and easy-to-use mathematical form. Since a number of such relationships may exist, for efficiency it is desirable that they are obtained in a single knowledge extraction step. Further, problem knowledge can be explored at two levels: lower and higher. At the lower-level, our interest is in finding a subset of the trade-off solutions to which the obtained relationships are exclusive. The higher-level knowledge addresses the effect of varying the problem parameters (that are kept constant in one run) on the trade-off front and therefore on the relationships. These concepts are explained through different examples.