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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lyles, W. Britt, W. Bevly, D. |
| Copyright Year | 2009 |
| Abstract | This paper demonstrates an evolutionary algorithm for the optimization of an autonomous control algorithm for a trained canine. Autonomous guidance is relevant because use of canines, though beneficial in many applications, is limited by the necessity of close human supervision. A rules-based expert system using GPS data was initially developed for this purpose. This rules-based system is not without limitations. Primarily, it takes a significant investment of trainer and developer time to derive appropriate values to use for control of the canine. A multi-objective fitness metric was developed to optimize for important parts of the control algorithm, and parameters of the algorithm were optimized using evolutionary computation. In simulations the evolved parameters fit the data better than the hand-tuned parameters, and preliminary field trials showed a 67% mission success rate, which shows the feasibility of evolving parameters for the control algorithm. |
| Starting Page | 699 |
| Ending Page | 704 |
| File Size | 237535 |
| Page Count | 6 |
| File Format | |
| ISBN | 9780769539263 |
| DOI | 10.1109/ICMLA.2009.100 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-12-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Machine learning algorithms Computational modeling Humans Evolutionary computation evolutionary algorithms Sensor phenomena and characterization Global Positioning System Canine control Investments Machine learning Error correction parameter optimization Expert systems |
| Content Type | Text |
| Resource Type | Article |
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