1. Austel, V., Dash, S., Gunluk, O., Horesh, L., Liberti, L., Nannicini, G., Schieber, B., 2017. Globally optimal symbolic regression. arXiv:1710.10720v1.
2. Tuning the parameters of an artificial neural network using central composite design and genetic algorithm;Bashiri;Sci. Iranica,2011
3. Classification and regression trees (CART);Berk,2016
4. Advances in surrogate based modeling, feasibility analysis, and optimization: a review;Bhosekar;Comput. Chem. Eng.,2018
5. A training algorithm for optimal margin classifiers;Boser,1992