1.Searson, D., Genetic Programming & Symbolic Regression for MATLAB User Guide, (2009)
2.聶渝,溶解度參数及其應用. 塗料工業, 10.3: 23-29 ,(2019).
3.郭宇靖,塑料用塗料的研究. 河北化工, 5: 9-10, (2002).
4.Bicerano, J., Prediction of Polymer Properties, Marcel Dekker, New York, (2002)
5.Inzelt, G., Conducting Polymers a New Era in Electrochemistry, Springer, Berlin, (2008)
6.Nanto ,N., Dougami ,N., T. Mukai, M. Habara, E. Kusano, A. Kinbara, T. Ogawa, T. Oyabu, A smart gas sensor using polymer-film coated quartz resonator microbalance, Sens. Actuators B, 66 ,16(2000).
7.Shevade, A.V., Ryan, M.A., Homer, M.L., Manfreda, A.M., Zhou, H., Manatt, K.S. Molecular modeling of polymer composite-analyte interactions in electronic nose sensors, Sens. Actuators B, 93, 84(2003).
8.Chen, X., Yuan, C., Wong, C.K.Y., Zhang, G., Molecular modeling of temperature depen-dence of solubility parameters for amorphous polymers, J. Mol. Model. 18 (2012) 2333–2341.
9.Yu, X., Huang, X., Prediction of Glass Transition Temperatures for Polystyrenes by a Four-Descriptors QSPR Model, Macromol. Theory Simul , 15, 94(2006)
10.Xu, J., Chen, B., Zhang, Q. and Guo , B., Prediction of refractive indices of linear polymers by a four-descriptor QSPR model. Polymer, 45, 8651(2004)
11.Koç, Dilek.; Koç İmren.; Levent Mehmet.; A genetic programming-based QSPR model for predicting solubility parameters of polymers, Chemometrics and Intelligent Laboratory Systems, 144, 122(2015)
12.Grosman, B., Lewin, D. R., Automated nonlinear model predictive control using genetic programming. Computers and Chemical Engineering, 26, 631(2002)
13.Koç D. İ., Koç, M. L., A genetic programming-based QSPR model for predicting solubility parameters of polymers. Chemometrics and Intelligent Laboratory Systems, 144, 122(2015)
14.林泓全,以遺傳規劃法模式估計聚苯乙烯高分子玻璃轉化温度,GeneticProgramming Based Models forPredicting Glass Transition Temperature of Polystyrenes,中國文化大學工學院化學工程與材料工程學系暨奈米材料碩士班 碩士學位論文(2021)15.Koza, J. R., Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87(1994)
16.Searson, D. P.,GPTIPS 2: an open-source software platform for symbolic data mining. New York: Springer.(2015)
17.Cevik, A., Cabalar, A. C., A Genetic–Programming–Based Formulation for the Strength Enhancement of Fiber–Reinforced–Polymer–Confined Concrete Cylinders. Journal of Applied Polymer Science , 110, 3087(2008)
18.Lim, Jian,C.; Karakus, Murat.; Ozbakkaloglu, Togay. ; Evaluation of ultimate conditions of FRP-confined concrete columns using genetic programming. Computers & Structures, 162: 28-37(2016)
19.Please refer to website (https://hackernoon.com/memorizing-is-not-learning-6-tricks-to-prevent-overfitting-in-machine-learning-820b091dc42)
20.張榮正;徐力行;陳榮傑,雙參數最佳生成樹之研究On Optimum Spanning Trees Of Two-Parameter Objectives,(1994).
21.Kara, I. F., Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming. Advances in Engineering Software, 42(6), 295-304 (2011)
22.Please refer to website (https://hachp1.github.io/posts/%E6%99%A8%E5%AD%A
6%E4%B9%A0/2019005-ROC_REC_PR.html)
23.徐林發;汪素珍;王柏省,應用 ROC 曲線求解最佳切點的方法介绍,28.6: 701-702(2011)
24.王彥光;朱鴻斌;徐維超.,ROC 曲線及其分析方法綜述,廣東工業大學學報,38.1: 46-53(2021)