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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems
verfasst von : Sunith Bandaru, Kalyanmoy Deb
Erschienen in: Evolutionary Multi-Criterion Optimization
Verlag: Springer Berlin Heidelberg
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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.