Learning an Optimization Algorithm through Human Design Iterations

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 the computer learns to mimic the search strategy of a human solver, so that the search can be continued after he/she abandons the problem.

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Related Papers
1. Sexton, T. and Ren, Y. (2017). Learning an Optimization Algorithm through Human Design Iterations. ASME Journal of Mechanical Design. arXiv , Link

2. Ren, Y., Bayrak, A. and Papalambros, P. Y. (2016). Ecoracer Optimal Design and Control of Electric Vehicles Using Human Game Players. Journal of Mechanical Design, 138(6), 061407. pdf