Results 271 to 280 of about 12,241,994 (329)
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Gaussian Process-Based Random Search for Continuous Optimization via Simulation
Operational Research, 2023A gaussian process-based random search framework for continuous optimization via simulation Stochastic optimization via simulation (OvS) is widely used for optimizing the performances of complex systems with continuous decision variables.
Xiuxian Wang +3 more
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2021 9th International Conference on Cyber and IT Service Management (CITSM), 2021
Being the most expensive and most popular cryptocurrency, both the business world and the research community have started to study bitcoin development.
Nurhayati Buslim +3 more
semanticscholar +1 more source
Being the most expensive and most popular cryptocurrency, both the business world and the research community have started to study bitcoin development.
Nurhayati Buslim +3 more
semanticscholar +1 more source
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
The manuscript addresses a robust three-dimensional fuzzy-PID controller (Fuzzy-3D PID) to control frequency of a diverse energy source integrated hybrid power system under various loadings.
S. Bhatta +4 more
semanticscholar +1 more source
The manuscript addresses a robust three-dimensional fuzzy-PID controller (Fuzzy-3D PID) to control frequency of a diverse energy source integrated hybrid power system under various loadings.
S. Bhatta +4 more
semanticscholar +1 more source
Comparative study of random search hyper-parameter tuning for software effort estimation
International Conference on Predictive Models in Software Engineering, 2021Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for the low dimensionality ...
Leonardo Villalobos-Arias +1 more
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30th Annual Symposium on Foundations of Computer Science, 1989
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seidel, Raimund, Aragon, Cecilia R.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Seidel, Raimund, Aragon, Cecilia R.
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IEEE Transactions on Computers, 1972
A random search technique for function minimization is proposed that incorporates the step-size and direction adaptivity of Hooke and Jeeves' [1] pattern search. Experimental results for a variety of functions indicate that the random pattern search is more effective than the corresponding deterministic method for a class of problems with hard ...
J. P. Lawrence, Kenneth Steiglitz
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A random search technique for function minimization is proposed that incorporates the step-size and direction adaptivity of Hooke and Jeeves' [1] pattern search. Experimental results for a variety of functions indicate that the random pattern search is more effective than the corresponding deterministic method for a class of problems with hard ...
J. P. Lawrence, Kenneth Steiglitz
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International Conference on Predictive Models in Software Engineering, 2020
Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates.
Leonardo Villalobos-Arias +4 more
semanticscholar +1 more source
Studies in software effort estimation (SEE) have explored the use of hyper-parameter tuning for machine learning algorithms (MLA) to improve the accuracy of effort estimates.
Leonardo Villalobos-Arias +4 more
semanticscholar +1 more source
Random Search One Dimensional CNN for Human Activity Recognition
International Conference on Computing and Information, 2020Due to its wide application, human activity recognition (HAR) has become a common subject for research specially with the development of deep learning.
M. G. Ragab +2 more
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A note on randomized mutual search
Information Processing Letters, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zvi Lotker, Boaz Patt-Shamir
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Hyper-Parameter Tuning based on Random Search for DenseNet Optimization
International Conference on Information Technology, Computer, and Electrical Engineering, 2020Deep learning is a machine learning technology that is currently experiencing rapid development. One of the deep learning architecture models is Densely Connected Convolutional Networks (DenseNet), which connects each layer along with feature maps to all
Arief Kelik Nugroho, H. Suhartanto
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