@incollection{,
	
					
					
					00D10C025E866979462F83DBBEDBD937
				,
	author={{Md RiyadHossain} and {Dr. DouglasTimmer} and {University of Texas Rio Grande Valley}},
	journal={{Global Journal of Computer Science and Technology}},
	journal={{GJCST}}0975-41720975-435010.34257/gjcst,
	address={Cambridge, United States},
	publisher={Global Journals Organisation}212713
}

@incollection{b0,
	
	
		,
	title={{Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks}}
		,
	author={{
			HCho
		} and {
			YKim
		} and {
			ELee
		} and {
			DChoi
		} and {
			YLee
		} and {
			WRhee
		}}
		
		
		
		
		
		10.1109/access.2020.2981072
	
	
		,
	journal={{IEEE Access}}
		
			8
			
			,
	year={2020}
		
	

}

@incollection{b1,
	
	
		,
	title={{Algorithms for hyperparameter optimization}}
		,
	author={{
			JSBergstra
		} and {
			RBardenet
		} and {
			YBengio
		} and {
			BKégl
		}}
		
		
		
	
	
		,
	booktitle={{Advances in Neural Information Processing Systems}}
				
			,
	year={2011}
			
		
	

}

@incollection{b2,
	
	
		,
	title={{Beyond manual tuning of hyperparameters}}
		,
	author={{
			FHutter
		} and {
			JLücke
		} and {
			LSchmidt-Thieme
		}}
		
		
	
	
		,
	journal={{DISKI}}
		
			29
			4
			
			,
	year={2015}
		
	

}

@incollection{b3,
	
	
		,
	title={{Evolutionary tuning of multiple SVM parameters}}
		,
	author={{
			FFriedrichs
		} and {
			CIgel
		}}
		
	
	
		,
	journal={{Neurocomputing}}
		
			64
			
			,
	year={2005}
		
	

}

@book{b4,
	
	
		,
	author={{
			RGMantovani
		} and {
			ALRossi
		} and {
			JVanschoren
		} and {
			BBischl
		} and {
			ACDe Carvalho
		}}
		
		
		
		
		,
	title={{2015 International Joint Conference on Neural Networks (IJCNN)}}
				
			,
	year={2015}
			
		
	
	,
	note={Effectiveness of random search in SVM hyper-parameter tuning}

}

@book{b5,
	
	
		,
	title={{Random search and reproducibility for neural architecture search}}
		,
	author={{
			LLi
		} and {
			ATalwalkar
		}}
		
		arXiv:1902.07638
		
			,
	year={2019}
		
	
	,
	note={arXiv preprint}

}

@incollection{b6,
	
	
		,
	title={{Towards an empirical foundation for assessing bayesian optimization of hyperparameters}}
		,
	author={{
			KEggensperger
		} and {
			MFeurer
		} and {
			FHutter
		} and {
			JBergstra
		} and {
			JSnoek
		} and {
			HHoos
		} and {
			KLeyton-Brown
		}}
		
		
		
		
		
		
	
	
		,
	booktitle={{NIPS workshop on Bayesian Optimization in Theory and Practice}}
				
			,
	year={2013}
			10
			3
		
	

}

@incollection{b7,
	
	
		,
	title={{An empirical evaluation of deep architectures on problems with many factors of variation}}
		,
	author={{
			HLarochelle
		} and {
			DErhan
		} and {
			ACourville
		} and {
			JBergstra
		} and {
			YBengio
		}}
		
		
		
		
	
	
		,
	booktitle={{Proceedings of the 24th International Conference on Machine Learning}}
				the 24th International Conference on Machine Learning
		
			,
	publisher={ACM}
			,
	year={2007}
			
		
	

}

@book{b8,
	
	
		,
	author={{
			JSnoek
		} and {
			ORippel
		} and {
			KSwersky
		} and {
			RKiros
		} and {
			NSatish
		} and {
			NSundaram
		}}
		
		
		
		
		
		,
	title={{Scalable bayesian optimization using deep neural networks, in: International conference on machine learning}}
				
			,
	year={2015}
			
		
	

}

@incollection{b9,
	
	
		,
	title={{Efficient global optimization of expensive black-box functions}}
		,
	author={{
			DRJones
		} and {
			MSchonlau
		} and {
			WJWelch
		}}
		
		
	
	
		,
	journal={{J. Glob. Optim}}
		
			13
			
			,
	year={1998}
		
	

}

@incollection{b10,
	
	
		,
	title={{Manifold Gaussian processes for regression}}
		,
	author={{
			RCalandra
		} and {
			JPeters
		} and {
			CERasmussen
		} and {
			MPDeisenroth
		}}
		
		
		
	
	
		,
	booktitle={{Proceedings of the 2016 International Joint Conference on Neural Networks}}
				the 2016 International Joint Conference on Neural NetworksVancouver, BC, Canada
		
			,
	year={July 2016}
			
		
	

}

@incollection{b11,
	
	
		,
	title={{A review of random search methods}}
		,
	author={{
			SAndrad_Ottir
		}}
	
	
		,
	booktitle={{Handbook of Simulation Optimization}}
				
			,
	publisher={Springer}
			,
	year={2015}
			
		
	

}

@incollection{b12,
	
	
		,
	title={{A Novel Bandit-Based Approach to Hyperparameter Optimization}}
		,
	author={{
			LLi
		} and {
			KJamieson
		} and {
			GDesalvo
		} and {
			ARostamizadeh
		} and {
			ATalwalker
		} and {
			Hyperband
		}}
		
		
		
		
		
	
	
		,
	journal={{Journal of Machine Learning Research}}
		
			18
			
			,
	year={2018}
		
	

}

@incollection{b13,
	
	
		,
	title={{Learning curve prediction with Bayesian neural networks}}
		,
	author={{
			AKlein
		} and {
			SFalkner
		} and {
			JTSpringenberg
		} and {
			FHutter
		}}
		
		
		
	
	
		,
	booktitle={{International Conference On Learning Representation (ICLR}}
				
			,
	year={2017}
		
	

}

@incollection{b14,
	
	
		,
	title={{Random Search for Hyper-Parameter Optimization}}
		,
	author={{
			JSBergstra
		} and {
			YBengio
		}}
		
	
	
		,
	journal={{Journal of Machine Learning Research}}
		
			13
			
			,
	year={2012}
		
	

}

@incollection{b15,
	
	
		,
	title={{Support Vector Regression Based on Grid-Search Method for Short-Term Wind Power Forecasting}}
		,
	author={{
			HZhang
		} and {
			LChen
		} and {
			YQu
		} and {
			GZhao
		} and {
			ZGuo
		}}
		
		
		
		
		10.1155/2014/835791
	
	
		,
	journal={{Journal of Applied Mathematics}}
		
			
			,
	year={2014. 2014}
		
	

}

@incollection{b16,
	
	
		,
	title={{Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity}}
		,
	author={{
			RGhawi
		} and {
			JPfeffer
		}}
		
		10.1515/comp-2019-0011
	
	
		,
	journal={{Open Computer Science}}
		
			9
			1
			
			,
	year={2019}
		
	

}

@incollection{b17,
	
	
		,
	title={{Investigation of the equality constraint effect on the reduction of the rotational ambiguity in threecomponent system using a novel grid search method}}
		,
	author={{
			SBeyramysoltan
		} and {
			RRajkó
		} and {
			HAbdollahi
		}}
		
		
		10.1016/j.aca.2013.06.043
	
	
		,
	journal={{Analytica Chimica Acta}}
		
			791
			
			,
	year={2013}
		
	

}

@incollection{b18,
	
	
		,
	title={{Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning}}
		,
	author={{
			XZhang
		} and {
			XChen
		} and {
			LYao
		} and {
			CGe
		} and {
			MDong
		}}
		
		
		
		
		10.1007/978-3-030-36808-1_31
	
	
		,
	booktitle={{Computer and Information Science Neural Information Processing}}
				
			,
	year={2019}
			
		
	

}

@incollection{b19,
	
	
		,
	title={{Crystalline behaviors and phase transition during the manufacture of fine denier PA6 fibers}}
		,
	author={{
			CZhang
		} and {
			YLiu
		} and {
			SLiu
		}}
		
		
		10.1007/s11426-009-0242-5
		
	
	
		,
	journal={{Sci. China Ser. B-Chem}}
		
			52
			1835
			,
	year={2009}
		
	

}

@incollection{b20,
	
	
		,
	title={{What Is Denier Rating? Why Does It Matter To You?}}
		,
	author={{
			Joe
		}}
		
	
	
		,
	journal={{Digi Travelist}}
		
			,
	year={2020. May 5}
		
	

}

@book{b21,
	
	
		,
	title={{Handbook of Properties of Textile and Technical Fibres || Tensile properties of cotton fibers}}
		,
	author={{
			YehiaElmogahzy
		}}
		10.1016/B978-0-08-101272-7.00007-9
		
			,
	year={2018}
			
		
	

}

@incollection{b22,
	
	
		,
	title={{Materials and design for sports apparel}}
		,
	author={{
			KBlair
		}}
		10.1533/9781845693664.1.60
	
	
		,
	journal={{Materials in Sports Equipment}}
		
			
			,
	year={2007}
		
	

}

@book{b23,
	
	
		,
	title={{Forming Processes. Manufacturing Process Selection Handbook}}
		,
	author={{
			KSwift
		} and {
			JBooker
		}}
		
		10.1016/b978-0-08-099360-7.00004-5
		
			,
	year={2013}
			
		
	

}

@incollection{b24,
	
	
		,
	title={{Basic Enhancement Strategies When Using Bayesian Optimization for Hyperparameter Tuning of Deep Neural Networks}}
		,
	author={{
			HCho
		} and {
			YKim
		} and {
			ELee
		} and {
			DChoi
		} and {
			YLee
		} and {
			WRhee
		}}
		
		
		
		
		
		10.1109/access.2020.2981072
	
	
		,
	journal={{IEEE Access}}
		
			8
			
			,
	year={2020}
		
	

}

@book{b25,
	
	
		,
	title={{On hyperparameter optimization of machine learning algorithms: Theory}}
		,
	author={{
			LYang
		} and {
			A&shami
		}}
		
		
			,
	year={2020}
		
	

}

@book{b26,
	
	
		,
	title={{Novel suboptimal approaches for hyperparameter tuning of deep neural network}}
		,
	author={{
			MAmirabadi
		} and {
			MKahaei
		} and {
			SNezamalhosseini
		}}
		
		
		10.1016/j.phycom.2020.101057
		
			,
	year={2020}
			,
	publisher={Physical Communication}
			41
			101057
		
	
	,
	note={under the shelf of optical communication}

}

@book{b27,
	
	
		,
	title={{Advances in Yarn Spinning Technology || Yarn structure and properties from different spinning techniques}}
		,
	author={{
			GKTyagi
		}}
		doi:10. 1533/9780857090218.1.119
		
			,
	year={2010}
			
		
	

}

@incollection{b28,
	
	
		,
	title={{}}
		10.1016/j.neucom.2020.07.061
	
	
		,
	journal={{Neurocomputing}}
		
			415
			
		
	

}

@book{b29,
	
	
		,
	title={{Continuous Model Selection for Large-Scale Recommender Systems. Handbook of Statistics Big Data Analytics}}
		,
	author={{
			SChan
		} and {
			P&treleaven
		}}
		
		10.1016/b978-0-444-63492-4.00005-8
		
			,
	year={2015}
			
		
	

}

@book{b30,
	
	
		,
	author={{
			WMenke
		}}
		10.1016/b978-0-12-397160-9.00009-6
		,
	title={{Nonlinear Inverse Problems. Geophysical Data Analysis: Discrete Inverse Theory}}
				
			,
	year={2012}
			
		
	

}

@incollection{b31,
	
	
		,
	title={{Steepest Ascent, Steepest Descent, and Gradient Methods}}
		,
	author={{
			RBrereton
		}}
		10.1016/b978-044452701-1.00037-5
	
	
		,
	journal={{Comprehensive Chemometrics}}
		
			
			,
	year={2009}
		
	

}

@incollection{b32,
	
	
		,
	title={{Random search for hyper-parameter optimization}}
		,
	author={{
			JBergstra
		} and {
			YBengio
		}}
		
		
	
	
		,
	journal={{J. Mach. Learn. Res}}
		1532-4435
		
			13
			
			,
	year={2012}
		
	

}

@book{b33,
	
	
		,
	title={{Hyper-Parameter Optimization: A Review of Algorithms and Applications}}
		,
	author={{
			TYu
		} and {
			HZhu
		}}
		
		
		
			,
	year={2020}
		
	

}

@book{b34,
	
	
		,
	author={{
			EHazan
		} and {
			AKlivans
		} and {
			YYuan
		}}
		
		
		arXiv:1706.00764
		
		,
	title={{Hyperparameter optimization: a spectral approach}}
				
			,
	year={2017}
		
	
	,
	note={arXiv preprint}

}

@book{b35,
	
	
		,
	title={{Automated Machine Learning Methods, Systems}}
		,
	author={{
			FHutter
		} and {
			LKotthoff
		} and {
			J&vanschoren
		}}
		
		
		
			,
	year={2019}
			,
	publisher={Springer International Publishing}
		
	

}

@incollection{b36,
	
	
		,
	title={{Gaussian Processes For Machine Learning}}
		,
	author={{
			MSeeger
		}}
		10.1142/s0129065704001899
	
	
		,
	journal={{International Journal of Neural Systems}}
		
			14
			02
			
			,
	year={2004}
		
	

}

@incollection{b37,
	
	
		,
	title={{Sequential Model-Based Optimization for General Algorithm Configuration}}
		,
	author={{
			FHutter
		} and {
			HHHoos
		} and {
			KLeyton-Brown
		}}
		
		
	
	
		,
	booktitle={{Lecture Notes in Computer Science}}
		,
	editor={Coello C.A.C.}
		
			6683
			,
	year={2011. 2011}
		
	
	,
	note={(eds) Learning and Intelligent Optimization}

}

@book{b38,
	
	
		,
	title={{}}
		,
	author={{
			Springer
		}}
		10.1007/978-3-642-25566-3_40
		
		
			,
	address={Berlin, Heidelberg}
		
	

}

@incollection{b39,
	
	
		,
	title={{Algorithms for hyper-parameter optimization}}
		,
	author={{
			JBergstra
		} and {
			RBardenet
		} and {
			YBengio
		} and {
			BKégl
		}}
		
		
		
	
	
		,
	journal={{Adv Neural Inf Process Syst (NIPS)}}
		
			24
			
			,
	year={2011}
		
	

}