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100                             Fred K. Gruber

                       Bäck,  T.,  &  Schütz,  M.  (1996).  Intelligent  mutation  rate  control  in  canonical  genetic
                          algorithms. Foundations of Intelligent Systems, 158-167.
                       Bäck,  T.,  Fogel,  D.,  &  Michalewicz,  Z.  (Eds.).  (2000).  Evolutionary  computation  1:
                          Basic algorithms and operators (Vol. 1). CRC press.
                       Bazaraa,  M.,  Sherali,  H.,  &  Shetty,  C.  (2013).  Nonlinear  programming:  theory  and
                          algorithms. John Wiley & Sons.
                       Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data
                          mining and knowledge discovery, 2(2), 121-167.
                       Burman,  P.  (1989).  A  comparative  study  of  ordinary  cross-validation,  v-fold  cross-
                          validation and the repeated learning-testing methods. Biometrika, 503-514.
                       Chapelle,  O.,  Vapnik,  V.,  Bousquet,  O.,  &  Mukherjee,  S.  (2002).  Choosing  multiple
                          parameters for support vector machines. Machine learning, 46(1), 131-159.
                       Chang,  C.,  &  Lin,  C.  (2011).  LIBSVM:  a  library  for  support  vector  machines.  ACM
                          Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.
                       Chen,  X.  (2003,  August).  Gene  selection  for  cancer  classification  using  bootstrapped
                          genetic algorithms and support vector machines. In Bioinformatics Conference, 2003.
                          CSB 2003. Proceedings of the 2003 IEEE (pp. 504-505). IEEE.
                       Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines.
                          University Press, 2000.
                       Demuth, H., Beale, M., & Hagan, M. (2008). Neural network toolbox™ 6. User’s guide,
                          37-55.
                       Dietterich,  T.  G.  (1998).  Approximate  statistical  tests  for  comparing  supervised
                          classification learning algorithms. Neural computation, 10(7), 1895-1923.
                       Duan,  K.,  Keerthi,  S.  S.,  &  Poo,  A.  N.  (2003).  Evaluation  of  simple  performance
                          measures for tuning SVM hyperparameters. Neurocomputing, 51, 41-59.
                       Dumitrescu,  D.,  Lazzerini,  B.,  Jain,  L.  C.,  &  Dumitrescu,  A.  (2000).  Evolutionary
                          computation. CRC press.
                       Eiben, A. E. (2003). Multiparent recombination in evolutionary computing. Advances in
                          evolutionary computing, 175-192.
                       Fishwick,  P.  A.,  &  Modjeski,  R.  B.  (Eds.).  (2012).  Knowledge-based  simulation:
                          methodology and application (Vol. 4). Springer Science & Business Media.
                       Frie, T. T., Cristianini, N., & Campbell, C. (1998, July). The kernel-adatron algorithm: a
                          fast  and  simple  learning  procedure  for  support  vector  machines.  In  Machine
                          Learning:  Proceedings  of  the  Fifteenth  International  Conference  (ICML'98)
                          (pp. 188-196).
                       Frohlich,  H.,  Chapelle,  O.,  &  Scholkopf,  B.  (2003,  November).  Feature  selection  for
                          support  vector  machines  by  means  of  genetic  algorithm.  In  Tools  with  Artificial
                          Intelligence,  2003.  Proceedings.  15th IEEE  International  Conference  on (pp.  142-
                          148). IEEE.
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