Abstract
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.
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Bandaru, S., Deb, K. (2011). Automated Innovization for Simultaneous Discovery of Multiple Rules in Bi-objective Problems. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds) Evolutionary Multi-Criterion Optimization. EMO 2011. Lecture Notes in Computer Science, vol 6576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19893-9_1
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DOI: https://doi.org/10.1007/978-3-642-19893-9_1
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