Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories.

ABSTRACT: Finding the ideal conditions satisfying multiple pre-defined targets simultaneously is a challenging decision-making process, which impacts science, engineering, and economics. Additional complexity arises for tasks involving experimentation or expensive computations, as the number of evaluated conditions must be kept low. We propose Chimera as a general purpose achievement scalarizing function for multi-target optimization where evaluations are the limiting factor. Chimera combines concepts of a priori scalarizing with lexicographic approaches and is applicable to any set of n unknown objectives. Importantly, it does not require detailed prior knowledge about individual objectives. The performance of Chimera is demonstrated on several well-established analytic multi-objectiv

SUBMITTER: Hase F 

PROVIDER: S-EPMC6182568 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature