Abstract
We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition and received 2391 game plays by 124 anonymous players during the first month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.
Original language | English (US) |
---|---|
Journal | Journal of Mechanical Design - Transactions of the ASME |
Volume | 138 |
Issue number | 6 |
DOIs | |
State | Published - Jun 1 2016 |
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering
- Computer Science Applications
- Computer Graphics and Computer-Aided Design