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Game AI hyperparameter tuning in rinascimento
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019Hyperparameter tuning is an important mixed-integer optimisation problem, especially in the context of real-world applications such as games. In this paper, we propose a function suite around hyperparameter optimisation of game AI based on the card game Splendor and using the Rinascimento framework.
Ivan Bravi, Vanessa Volz, Simon M. Lucas
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Sequential Model-Free Hyperparameter Tuning
2015 IEEE International Conference on Data Mining, 2015Hyperparameter tuning is often done manually but current research has proven that automatic tuning yields effective hyperparameter configurations even faster and does not require any expertise. To further improve the search, recent publications propose transferring knowledge from previous experiments to new experiments.
Martin Wistuba +2 more
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An Approach to Tuning Hyperparameters in Parallel: An Approach to Tuning Hyperparameters in Parallel
2019Predicting violent storms and dangerous weather conditions, for instance predicting tornados, is an important application for public safety. Using numerical weather simulations to classify a weather pattern as tornadic or not tornadic can take a long time due to the immense complexity associated with current models.
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Tuning SVM hyperparameters in the primal
2010 Second International Conference on Computational Intelligence and Natural Computing, 2010Choosing optimal hyperparameters for Support Vector Machines(SVMs) is quite difficult but extremely essential in SVM design. This is usually done by minimizing estimates of generalization error such as the k-fold cross-validation error or the upper bound of leave-one-out(LOO) error.
null Huang Dongyuan, null Chen Xiaoyun
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Kriging Hyperparameter Tuning Strategies
AIAA Journal, 2008Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelity computational simulations. Of the numerous types of response surface models, kriging is perhaps one of the most effective, due to its ability to model complicated responses through interpolation or regression of known data while providing an
Toal, David J.J. +2 more
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Hyperparameter Tuning in Offline Reinforcement Learning
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022Andrew Tittaferrante, Abdulsalam Yassine
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Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting
Remote Sensing, 2023Panagiotis Kosmopoulos +2 more
exaly
Tuning ForestDisc Hyperparameters: A Sensitivity Analysis
2022Maissae Haddouchi, Abdelaziz Berrado
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Improving the Robustness and Quality of Biomedical CNN Models through Adaptive Hyperparameter Tuning
Applied Sciences (Switzerland), 2022Saeed Iqbal +2 more
exaly