TY - GEN
T1 - Design preference elicitation, derivative-free optimization and support vector machine search
AU - Ren, Yi
AU - Papalambros, Panos Y.
PY - 2010
Y1 - 2010
N2 - In design preference elicitation, we seek to find individuals' design preferences, usually through an interactive process that would need only a very small number of interactions. Such a process is akin to an optimization algorithm that operates with point values of an unknown function and converges in a small number of iterations. In this paper, we assume the existence of individual preference functions and show that the elicitation task can be translated into a derivative-free optimization (DFO) problem. Different from commonly-studied DFO formulations, we restrict the outputs to binary classes discriminating sample points with higher function values from those with lower values, to capture people's natural way of expressing preferences through comparisons. To this end, we propose a heuristic search algorithm using support vector machines (SVM) that can locate near-optimal solutions with a limited number of iterations and a small sampling size. Early experiments with test functions show reliable performance when the function is not noisy. Further, SVM search appears promising in design preference elicitation when the dimensionality of the design variable domain is relatively high.
AB - In design preference elicitation, we seek to find individuals' design preferences, usually through an interactive process that would need only a very small number of interactions. Such a process is akin to an optimization algorithm that operates with point values of an unknown function and converges in a small number of iterations. In this paper, we assume the existence of individual preference functions and show that the elicitation task can be translated into a derivative-free optimization (DFO) problem. Different from commonly-studied DFO formulations, we restrict the outputs to binary classes discriminating sample points with higher function values from those with lower values, to capture people's natural way of expressing preferences through comparisons. To this end, we propose a heuristic search algorithm using support vector machines (SVM) that can locate near-optimal solutions with a limited number of iterations and a small sampling size. Early experiments with test functions show reliable performance when the function is not noisy. Further, SVM search appears promising in design preference elicitation when the dimensionality of the design variable domain is relatively high.
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U2 - 10.1115/DETC2010-28475
DO - 10.1115/DETC2010-28475
M3 - Conference contribution
AN - SCOPUS:80054978357
SN - 9780791844090
T3 - Proceedings of the ASME Design Engineering Technical Conference
SP - 335
EP - 343
BT - ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2010
T2 - ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2010
Y2 - 15 August 2010 through 18 August 2010
ER -