Aarts E, Lenstra J. (1997). Local search in combinatorial optimization.

Braun H, Riedmiller M. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm Proc IEEE Int Conf Neural Networks. 16

Fahlman S. (1988). An empirical study of learning speed in back-propagation networks Tech. Rep. No. CMU-CS-88-162.

Igel C, Husken M. (2000). Improving the Rprop learning algorithm Proceedings Of The Second International Symposium On Neural Computation.

Jacobs R. (1988). Increased rates of convergence through learning rate adaption Neural Netw. 1

Kirkpatrick S, Gelatt CD, Vecchi MP. (1983). Optimization by simulated annealing. Science (New York, N.Y.). 220 [PubMed]

Moller M. (1993). A scaled conjugate gradient algorithm for fast supervised learning Neural Netw. 6

Prechelt L. (1994). PROBEN1--A set of benchmarks and benchmarking rules for neural network training algorithms Tech. Rep. No. 21-94.

Riedmiller M. (1994). Advanced supervised learning in multi-layer perceptrons--from backpropagation to adaptive learning algorithms Computer Standards And Interfaces. 16

Rumelhart D, Mccleland J. (1986). Parallel Distributed Processing.

Salhi S, Queen N. (2004). Determining local and global minima for functions with multiple minima Euro J Op Res.

Schiffman W, Joost M, Werner R. (1993). Optimization of the backpropagation algorithm for training multilayer perceptrons Tech Rep.

Silva F, Almeida L. (1990). Advanced neural computers.

Tollenaere T. (1990). Supersab: Fast adaptive backpropagation with good scaling properties Neural Netw. 3