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research-article

Learning an Optimization Algorithm through Human Design Iterations

[+] Author and Article Information
Thurston Sexton

National Institute of Standards and Technology
tbsexton@asu.edu

Yi Ren

Department of Mechanical Engineering, Arizona State University
yiren@asu.edu

1Corresponding author.

ASME doi:10.1115/1.4037344 History: Received December 01, 2016; Revised July 19, 2017

Abstract

Solving optimal design problems through crowdsourcing faces a dilemma: On one hand, human beings have been shown to be more effective than algorithms at searching for good solutions of certain real-world problems with high-dimensional or discrete solution spaces; on the other hand, the cost of setting up crowdsourcing environments, the uncertainty in the crowd's domain-specific competence, and the lack of commitment of the crowd, all contribute to the lack of real-world application of design crowdsourcing. We are thus motivated to investigate a solution-searching mechanism where an optimization algorithm is tuned based on human demonstrations on solution searching, so that the search can be continued after human participants abandon the problem. To do so, we model the iterative search process of a human solver as a Bayesian Optimization (BO) algorithm, and propose an inverse BO (IBO) algorithm to find the maximum likelihood estimators of the BO parameters based on human solutions. We show through a vehicle design and control problem that the search performance of BO can be improved by recovering its parameters based on an effective human search. Thus, IBO has the potential to improve the success rate of design crowdsourcing activities, by requiring only good search strategies instead of good solutions from the crowd.

Copyright (c) 2017 by ASME
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