@inproceedings{lyles_evolution_2009,
	title = {Evolution of {Parameters} for an {Autonomous} {Canine} {Control} {Algorithm}},
	url = {https://ieeexplore.ieee.org/abstract/document/5381343},
	doi = {10.1109/ICMLA.2009.100},
	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.},
	urldate = {2024-06-20},
	booktitle = {2009 {International} {Conference} on {Machine} {Learning} and {Applications}},
	author = {Lyles, William and Britt, Winard and Bevly, David},
	month = dec,
	year = {2009},
	keywords = {Canine control, Computational modeling, Error correction, Evolutionary computation, Expert systems, Global Positioning System, Humans, Investments, Machine learning, Machine learning algorithms, Sensor phenomena and characterization, evolutionary algorithms, parameter optimization},
	pages = {699--704},
}

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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.","urldate":"2024-06-20","booktitle":"2009 International Conference on Machine Learning and Applications","author":[{"propositions":[],"lastnames":["Lyles"],"firstnames":["William"],"suffixes":[]},{"propositions":[],"lastnames":["Britt"],"firstnames":["Winard"],"suffixes":[]},{"propositions":[],"lastnames":["Bevly"],"firstnames":["David"],"suffixes":[]}],"month":"December","year":"2009","keywords":"Canine control, Computational modeling, Error correction, Evolutionary computation, Expert systems, Global Positioning System, Humans, Investments, Machine learning, Machine learning algorithms, Sensor phenomena and characterization, evolutionary algorithms, parameter optimization","pages":"699–704","bibtex":"@inproceedings{lyles_evolution_2009,\n\ttitle = {Evolution of {Parameters} for an {Autonomous} {Canine} {Control} {Algorithm}},\n\turl = {https://ieeexplore.ieee.org/abstract/document/5381343},\n\tdoi = {10.1109/ICMLA.2009.100},\n\tabstract = {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.},\n\turldate = {2024-06-20},\n\tbooktitle = {2009 {International} {Conference} on {Machine} {Learning} and {Applications}},\n\tauthor = {Lyles, William and Britt, Winard and Bevly, David},\n\tmonth = dec,\n\tyear = {2009},\n\tkeywords = {Canine control, Computational modeling, Error correction, Evolutionary computation, Expert systems, Global Positioning System, Humans, Investments, Machine learning, Machine learning algorithms, Sensor phenomena and characterization, evolutionary algorithms, parameter optimization},\n\tpages = {699--704},\n}\n\n\n\n","author_short":["Lyles, W.","Britt, W.","Bevly, D."],"key":"lyles_evolution_2009","id":"lyles_evolution_2009","bibbaseid":"lyles-britt-bevly-evolutionofparametersforanautonomouscaninecontrolalgorithm-2009","role":"author","urls":{"Paper":"https://ieeexplore.ieee.org/abstract/document/5381343"},"keyword":["Canine control","Computational modeling","Error correction","Evolutionary computation","Expert systems","Global Positioning System","Humans","Investments","Machine learning","Machine learning algorithms","Sensor phenomena and characterization","evolutionary algorithms","parameter optimization"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero-group/keb0115/5574615","dataSources":["kDK6fZ4EDThxNKDCP"],"keywords":["canine control","computational modeling","error correction","evolutionary computation","expert systems","global positioning system","humans","investments","machine learning","machine learning algorithms","sensor phenomena and characterization","evolutionary algorithms","parameter optimization"],"search_terms":["evolution","parameters","autonomous","canine","control","algorithm","lyles","britt","bevly"],"title":"Evolution of Parameters for an Autonomous Canine Control Algorithm","year":2009}