Journal of Global Optimization
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A hybrid method for solving multi-objective global optimization problems
Journal of Global Optimization
An interactive solution approach for a bi-objective semi-desirable location problem
Journal of Global Optimization
Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes
Engineering Applications of Artificial Intelligence
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A variational approach to define robustness for parametric multiobjective optimization problems
Journal of Global Optimization
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Innovization (innovation through optimization) is a relatively new concept in the field of multi-objective engineering design optimization. It involves the use of Pareto-optimal solutions of a problem to unveil hidden mathematical relationships between variables, objectives and constraint functions. The obtained relationships can be thought of as essential properties that make a feasible solution Pareto-optimal. This paper proposes two major extensions to innovization, namely higher-level innovization and lower-level innovization. While the former deals with the discovery of common features among solutions from different Pareto-optimal fronts, the latter concerns features commonly occurring among solutions that belong to a specified (or preferred) part of the Pareto-optimal front. The knowledge of such lower-level information is extremely beneficial to a decision maker, since it focuses on a preferred set of designs. On the other hand, higher-level innovization reveals interesting knowledge about the general problem structure. Neither of these crucial aspects concerning multi-objective designs has been addressed before, to the authors' knowledge. We develop methodologies for handling both levels of innovization by extending the authors' earlier automated innovization algorithm and apply them to two well-known engineering design problems. Results demonstrate that the proposed methodologies are generic and are ready to be applied to other engineering design problems.