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A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods
(Scitepress, 2017-01-01)
The fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both ...
A statistical approach for the fine-tuning of metaheuristics: A case study combining design of experiments and racing algorithms
(2015-01-01)
The fine-tuning of heuristics and metaheuristics exercises a great influence in both the solution process, as well as in the quality of results of optimization problems. The search for the best fit of these algorithms is ...
Robustness/performance tradeoff for anisochronic plants with two degrees of freedom PID controllers
(2015)
In this paper, the anisochronic model (which is able to represent both over-damped and under-damped process with the same topology) is used for the tuning of PI and PID controllers. The relationship between robustness and ...
The use of tuned filters as an attenuator device of harmonics generated by multipulse converters
(2011-05-31)
The purpose of this paper is to present a computer model that enables the operation analysis of a tuned filter as an attenuator device of harmonic generated 12 and 18-pulses converters with Y-generalized differential ...
Improving Pre- Trained Weights through Meta - Heuristics Fine- Tuning
(2021-01-01)
Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, ...
Power flow optimization for grid connected inverter using evolutionary algorithm and additional control loop
(2011-12-28)
In this work it is proposed to validate an evolutionary tuning algorithm in plants composed by a grid connected inverter. The optimization aims the tuning of the slopes of P-Ω and Q-V curves so that the system is stable, ...
To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning
(International Neural Network Society – INNSIEEE Computational Intelligence SocietyKillarney, 2015-07)
Many classification algorithms, such as Neural Networks and Support Vector Machines, have a range of hyperparameters that may strongly affect the predictive performance of the models induced by them. Hence, it is recommended ...
To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning
(2015-09-28)
Many classification algorithms, such as Neural Networks and Support Vector Machines, have a range of hyper-parameters that may strongly affect the predictive performance of the models induced by them. Hence, it is recommended ...
To tune or not to tune: recommending when to adjust SVM hyper-parameters via Meta-learning
(Ieee, 2015-01-01)
Many classification algorithms, such as Neural Networks and Support Vector Machines, have a range of hyperparameters that may strongly affect the predictive performance of the models induced by them. Hence, it is recommended ...
Fine Tuning Deep Boltzmann Machines Through Meta-Heuristic Approaches
(Ieee, 2018-01-01)
The Deep learning framework has been widely used in different applications from medicine to engineering. However, there is a lack of works that manage to deal with the issue of hyperparameter fine-tuning, since machine ...