Poster

Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design

Gustavo Malkomes · Bolong Cheng · Eric Lee · Michael McCourt

Keywords: [ Gaussian Processes and Bayesian non-parametrics ]

[ Abstract ]
[ Slides [ Paper ] [ Visit Poster at Spot A6 in Virtual World ]
Thu 22 Jul 9 a.m. PDT — 11 a.m. PDT
 
Spotlight presentation: Bayesian Learning 1
Thu 22 Jul 6 a.m. PDT — 7 a.m. PDT

Abstract:

Many problems in engineering design and simulation require balancing competing objectives under the presence of uncertainty. Sample-efficient multiobjective optimization methods focus on the objective function values in metric space and ignore the sampling behavior of the design configurations in parameter space. Consequently, they may provide little actionable insight on how to choose designs in the presence of metric uncertainty or limited precision when implementing a chosen design. We propose a new formulation that accounts for the importance of the parameter space and is thus more suitable for multiobjective design problems; instead of searching for the Pareto-efficient frontier, we solicit the desired minimum performance thresholds on all objectives to define regions of satisfaction. We introduce an active search algorithm called Expected Coverage Improvement (ECI) to efficiently discover the region of satisfaction and simultaneously sample diverse acceptable configurations. We demonstrate our algorithm on several design and simulation domains: mechanical design, additive manufacturing, medical monitoring, and plasma physics.

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