Optimized particle swarm optimization (OPSO) and its application to artificial neural network training
Michael Meissner, Michael Schmuker, and Gisbert Schneider · 2006
Cited alongside, same era.
Tuning the structure and parameters of a neural network by using hybrid taguchi-genetic algorithm
Jinn-Tsong Tsai, Jyh-Horng Chou, and Tung-Kuan Liu · 2006
Cited alongside, same era.
Support vector machine solvers
Léon Bottou and Chih-Jen Lin · 2007
Cited alongside, same era.
Particle swarm optimization for parameter determination and feature selection of support vector machines
Shih-Wei Lin, Kuo-Ching Ying, Shih-Chieh Chen, and Zne-Jung Lee · 2008
Cited alongside, same era.
ParamILS: an automatic algorithm configuration framework
Frank Hutter, Holger H Hoos, Kevin Leyton-Brown, and Thomas Stützle · 2009
Cited alongside, same era.
F-race and iterated f-race: An overview
Mauro Birattari, Zhi Yuan, Prasanna Balaprakash, and Thomas Stützle · 2010
Cited alongside, same era.
Coupled simulated annealing
Original
Samuel Xavier-de Souza, Johan AK Suykens, Joos Vandewalle, and Désiré Bollé · 2010
Cited alongside, same era.
Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
Cited alongside, same era.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
Cited alongside, same era.