Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Principles of Optimal Design
Pattern identification in pareto-set approximations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exploring new horizons in evolutionary design of robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Visualization and data mining of Pareto solutions using self-organizing map
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multi-objective evolutionary algorithms for resource allocation problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Design knowledge extraction in multi-objective optimization problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Temporal evolution of design principles in engineering systems: analogies with human evolution
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Higher and lower-level knowledge discovery from Pareto-optimal sets
Journal of Global Optimization
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The trade-off solutions of a multi-objective optimization problem, as a whole, often hold crucial information in the form of rules. These rules, if predominantly present in most trade-off solutions, can be considered as the characteristic features of the Pareto-optimal front. Knowledge of such features, in addition to providing better insights to the problem, enables the designer to handcraft solutions for other optimization tasks which are structurally similar to it; thus eliminating the need to actually optimize. Innovization is the process of extracting these so called design rules. This paper proposes to move a step closer towards the complete automation of the innovization process through a niched clustering based optimization technique. The focus is on obtaining multiple design rules in a single knowledge discovery step using the niching strategy.