A Review of Genetic Programming: Popular Techniques, Fundamental Aspects, Software Tools and Applications

Yıl 2021, Cilt: 25 Sayı: 2, 397 - 416, 15.04.2021

Davut Arı , Barış Baykant Alagöz

https://doi.org/10.16984/saufenbilder.793333

Cited By: 4

Öz

Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.

Anahtar Kelimeler

genetic programming, gp types, gp applications, gp software

Kaynakça

• [1] J. R. Koza, ‘Genetic programming: on the programming of computers by means of natural selection’, MIT press.,1992.
• [2] R. Poli, W. B. Langdon, and N. F. McPhee, ‘A Field Guide to Genetic Programing’, no. March,Lulu Enterprises, UK Ltd, 2008.
• [3] M. Amir Haeri, M. M. Ebadzadeh, and G. Folino, ‘Statistical genetic programming for symbolic regression’, Appl. Soft Comput., vol. 60, ,pp. 447–469, 2017.
• [4] M. O’Neill, L. Vanneschi, S. Gustafson, and W. Banzhaf, ‘Open issues in Genetic Programming’, Genet. Program. Evolvable Mach., vol. 11, no. 3–4, ,pp. 339–363, 2010.
• [5] A. Cano and S. Ventura, ‘GPU-parallel subtree interpreter for genetic programming’, Proceedings of the 2014 conference on Genetic and evolutionary computation - GECCO ’14, no. July, New York, New York, USA,ACM Press,pp. 887–894, 2014.
• [6] L. F. Dal Piccol Sotto and V. V. De Melo, ‘Investigation of linear genetic programming techniques for symbolic regression’, Proc. - 2014 Brazilian Conf. Intell. Syst. BRACIS 2014, ,pp. 146–151, 2014.
• [7] B. Tran, B. Xue, and M. Zhang, ‘Genetic programming for feature construction and selection in classification on high-dimensional data’, Memetic Comput., vol. 8, no. 1, ,pp. 3–15, 2016.
• [8] S. Nguyen, Y. Mei, and M. Zhang, ‘Genetic programming for production scheduling: a survey with a unified framework’, Complex Intell. Syst., vol. 3, no. 1, ,pp. 41–66, 2017.
• [9] P.-S. Ashofteh, O. B. Haddad, and H. A. Loáiciga, ‘Evaluation of Climatic-Change Impacts on Multiobjective Reservoir Operation with Multiobjective Genetic Programming’, J. Water Resour. Plan. Manag., vol. 141, no. 11, ,p. 04015030, 2015.
• [10] M. Castelli, L. Trujillo, L. Vanneschi, and A. Popovič, ‘Prediction of energy performance of residential buildings: A genetic programming approach’, Energy Build., vol. 102, ,pp. 67–74, 2015.
• [11] A. Tahmassebi and A. H. Gandomi, ‘Building energy consumption forecast using multi-objective genetic programming’, Measurement, vol. 118, no. January, ,pp. 164–171, 2018.
• [12] S. H. A. Kaboli, A. Fallahpour, J. Selvaraj, and N. A. Rahim, ‘Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming’, Energy, vol. 126, ,pp. 144–164, 2017.
• [13] A. Zafra and S. Ventura, ‘Multi-instance genetic programming for predicting student performance in web based educational environments’, Appl. Soft Comput., vol. 12, no. 8, ,pp. 2693–2706, 2012.
• [14] A. K. Patnaik and P. K. Bhuyan, ‘Application of genetic programming clustering in defining LOS criteria of urban street in Indian context’, Travel Behav. Soc., vol. 3, ,pp. 38–50, 2016.
• [15] A. Keshavarz and M. Mehramiri, ‘New Gene Expression Programming models for normalized shear modulus and damping ratio of sands’, Eng. Appl. Artif. Intell., vol. 45, ,pp. 464–472, 2015.
• [16] M. Shoaib, A. Y. Shamseldin, B. W. Melville, and M. M. Khan, ‘Runoff forecasting using hybrid Wavelet Gene Expression Programming (WGEP) approach’, J. Hydrol., vol. 527, ,pp. 326–344, 2015.
• [17] I. De Falco, A. Della Cioppa, T. Koutny, M. Krcma, U. Scafuri, and E. Tarantino, ‘Genetic Programming-based induction of a glucose-dynamics model for telemedicine’, J. Netw. Comput. Appl., vol. 119, no. December 2017, ,pp. 1–13, 2018.
• [18] L. Liu, L. Shao, X. Li, and K. Lu, ‘Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach’, IEEE Trans. Cybern., vol. 46, no. 1, ,pp. 158–170, 2016.
• [19] L. Shao, L. Liu, and X. Li, ‘Feature learning for image classification via multiobjective genetic programming’, IEEE Trans. Neural Networks Learn. Syst., vol. 25, no. 7, ,pp. 1359–1371, 2014.
• [20] M. Feli and F. Abdali-Mohammadi, ‘A novel recursive backtracking genetic programming-based algorithm for 12-lead ECG compression’, Signal, Image Video Process., vol. 13, no. 5, ,pp. 1029–1036, 2019.
• [21] M. Suganuma, S. Shirakawa, and T. Nagao, ‘A genetic programming approach to designing convolutional neural network architectures’, Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO ’17, New York, New York, USA,ACM Press,pp. 497–504, 2017.
• [22] J. Mora, R. Salvador, and E. de la Torre, ‘On the scalability of evolvable hardware architectures: comparison of systolic array and Cartesian genetic programming’, vol. 20, no. 2,Springer US, 2019.
• [23] M. Sikulova, G. Komjathy, and L. Sekanina, ‘Towards compositional coevolution in evolutionary circuit design’, IEEE SSCI 2014 - 2014 IEEE Symp. Ser. Comput. Intell. - IEEE ICES 2014 IEEE Int. Conf. Evolvable Syst. Proc., ,pp. 157–164, 2014.
• [24] H. Li and M. L. Wong, ‘Financial Fraud Detection by using Grammar-based Multi-objective Genetic Programming with ensemble learning’, 2015 IEEE Congr. Evol. Comput. CEC 2015 - Proc., ,pp. 1113–1120, 2015.
• [25] A. Fallahpour, E. U. Olugu, S. N. Musa, D. Khezrimotlagh, and K. Y. Wong, ‘An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach’, Neural Comput. Appl., vol. 27, no. 3, ,pp. 707–725, 2016.
• [26] M. W. Khan and M. Alam, ‘A survey of application: Genomics and genetic programming, a new frontier’, Genomics, vol. 100, no. 2, ,pp. 65–71, 2012.
• [27] R. Poli, N. F. McPhee, and L. Vanneschi, ‘Elitism reduces bloat in genetic programming’, GECCO’08 Proc. 10th Annu. Conf. Genet. Evol. Comput. 2008, ,pp. 1343–1344, 2008.
• [28] R. Poli, W. B. Langdon, N. F. McPhee, and J. R. Koza, ‘Genetic programming an introductory tutorial and a survey of techniques and applications’, Tech Rep CES475, vol. 18, no. October 2007, ,pp. 1–112, 2007.
• [29] P. T. J.F. Miller, ‘Cartesian genetic programming’, Proceedings of the European Conference on Genetic Programming, ,pp. 121–132, 2000.
• [30] M. Brameier and W. Banzhaf, ‘Evolving Teams of Predictors with Linear Genetic Programming’, Genet. Program. Evolvable Mach., vol. 2, no. 4, ,pp. 381–407, 2001.
• [31] J. F. Miller, ‘An empirical study of the efficiency of learning boolean functions using a Cartesian Genetic Programming approach’, Proc. Genet. Evol. Comput. Conf., vol. 2, ,pp. 1135--1142, 1999.
• [32] J. F. Miller, ‘Cartesian Genetic Programming’, Berlin, Heidelberg,Springer Berlin Heidelberg,pp. 17–34, 2011.
• [33] J. F. Miller, ‘Cartesian genetic programming: its status and future’, no. April,Springer US, 2019.
• [34] A. J. Turner and J. F. Miller, ‘Recurrent Cartesian Genetic Programming of Artificial Neural Networks’, Genet. Program. Evolvable Mach., vol. 18, no. 2, ,pp. 185–212, 2017.
• [35] A. J. Turner and J. F. Miller, ‘Recurrent Cartesian Genetic Programming’, ,pp. 476–486, 2014.
• [36] A. Manazir and K. Raza, ‘Recent developments in cartesian genetic programming and its variants’, ACM Comput. Surv., vol. 51, no. 6, 2019.
• [37] M. Brameier and W. Banzhaf, ‘A comparison of linear genetic programming and neural networks in medical data mining’, IEEE Trans. Evol. Comput., vol. 5, no. 1, ,pp. 17–26, 2001.
• [38] H. Shavandi and S. Saeedi Ramyani, ‘A linear genetic programming approach for the prediction of solar global radiation’, Neural Comput. Appl., vol. 23, no. 3–4, ,pp. 1197–1204, 2013.
• [39] T. Hu, J. L. Payne, W. Banzhaf, and J. H. Moore, Genetic Programming and Evolvable Machines, vol. 13, no. 3. ,pp. 305–337, ,pp. 305–337, 2012.
• [40] W. Banzhaf, F. D. Francone, R. E. Keller, and P. Nordin., ‘Genetic Programming: An Introduction.’,San Francisco, CA,Morgan Kaufmann Publishers, 1998.
• [41] A. Guven and O. Kisi, ‘Monthly pan evaporation modeling using linear genetic programming’, J. Hydrol., vol. 503, ,pp. 178–185, 2013.
• [42] T. Perkis, ‘Stack-based genetic programming’, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, IEEE,pp. 148–153, 1994.
• [43] D. S. P. Elko B. Tchernev, ‘Control structures in linear and stack-based Genetic Programming’, Late Breaking Papers at the 2004 Genetic and Evolutionary Computation Conference, Seattle, Washington, USA., 2004.
• [44] D. J. Montana, ‘Strongly Typed Genetic Programming’, Evol. Comput., vol. 3, no. 2, ,pp. 199–230, 1995.
• [45] T. D. Haynes, D. A. Schoenefeld, and R. L. Wainwright, ‘Type Inheritance in Strongly Typed Genetic Programming’, 1996.
• [46] G. W. Hinchliffe, M. P., Willis, M. J., Hiden, H., Tham, M. T., McKay, B., & Barton, ‘Modelling chemical process systems using a multi-gene genetic programming algorithm’, In Genetic Programming: Proceedings of the First Annual Conference, ,pp. 56–65, 1996.
• [47] A. H. Gandomi and A. H. Alavi, ‘A new multi-gene genetic programming approach to nonlinear system modeling. Part I: Materials and structural engineering problems’, Neural Comput. Appl., vol. 21, no. 1, ,pp. 171–187, 2012.
• [48] W. M. Searson DP, Leahy DE, ‘GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression’, International MultiConference of Engineers and Computer Scientists 2010 (IMECS), ,pp. 77–80, 2010.
• [49] D. Searson, M. Willis, and G. Montague, ‘Co-evolution of non-linear PLS model components’, J. Chemom., vol. 21, no. 12, ,pp. 592–603, 2007.
• [50] R. Dai, Y. Gao, S. Huang, F. Ning, and Z. Feng, ‘Multi-objective Genetic Programming based Automatic Modulation Classification’, 2019 IEEE Wireless Communications and Networking Conference (WCNC), vol. 2019-April, IEEE,pp. 1–6, 2019.
• [51] A. H. Gandomi, S. Sajedi, B. Kiani, and Q. Huang, ‘Genetic programming for experimental big data mining: A case study on concrete creep formulation’, Autom. Constr., vol. 70, ,pp. 89–97, 2016.
• [52] C. Ferreira, ‘Gene Expression Programming: a New Adaptive Algorithm for Solving Problems’, ,pp. 1–22, 2001.
• [53] A. Ş. Şahin, E. Dikmen, and S. Şentürk, ‘A gene expression programming approach for thermodynamic properties of working fluids used on Organic Rankine Cycle’, Neural Comput. Appl., vol. 31, no. 8, ,pp. 3947–3955, 2019.
• [54] C. Ferreira, ‘Gene Expression Programming’, vol. 21,Springer Berlin Heidelberg, 2006.
• [55] Man Leung Wong and Kwong Sak Leung, ‘Inducing logic programs with genetic algorithms: the Genetic Logic Programming System’, IEEE Expert, vol. 10, no. 5, ,pp. 68–76, 1995.
• [56] M. L. Wong and K. S. Leung, ‘Evolutionary Program Induction Directed by Logic Grammars’, Evol. Comput., vol. 5, no. 2, ,pp. 143–180, 1997.
• [57] R. I. McKay, N. X. Hoai, P. A. Whigham, Y. Shan, and M. O’neill, ‘Grammar-based Genetic programming: A survey’, Genet. Program. Evolvable Mach., vol. 11, no. 3–4, ,pp. 365–396, 2010.
• [58] R. V. Veiga, H. J. C. Barbosa, H. S. Bernardino, J. M. Freitas, C. A. Feitosa, S. M. A. Matos, N. M. Alcântara-Neves, and M. L. Barreto, ‘Multiobjective grammar-based genetic programming applied to the study of asthma and allergy epidemiology’, BMC Bioinformatics, vol. 19, no. 1, ,pp. 1–16, 2018.
• [59] M. O’Neill and C. Ryan, ‘Grammatical evolution’, IEEE Trans. Evol. Comput., vol. 5, no. 4, ,pp. 349–358, 2001.
• [60] J. M. Colmenar, J. I. Hidalgo, and S. Salcedo-Sanz, ‘Automatic generation of models for energy demand estimation using Grammatical Evolution’, Energy, vol. 164, ,pp. 183–193, 2018.
• [61] C. Martín, D. Quintana, and P. Isasi, ‘Evolution of trading strategies with flexible structures: A configuration comparison’, Neurocomputing, vol. 331, ,pp. 242–262, 2019.
• [62] I. Fajfar, Á. Bűrmen, and J. Puhan, ‘Grammatical evolution as a hyper-heuristic to evolve deterministic real-valued optimization algorithms’, vol. 19, no. 4,Springer US, 2018.
• [63] A. Moraglio, K. Krawiec, and C. G. Johnson, ‘Geometric Semantic Genetic Programming’, Berlin,Springer, Berlin, Heidelberg,pp. 21–31, 2012.
• [64] Q. Chen, B. Xue, and M. Zhang, ‘Improving Generalization of Genetic Programming for Symbolic Regression with Angle-Driven Geometric Semantic Operators’, IEEE Trans. Evol. Comput., vol. 23, no. 3, ,pp. 488–502, 2019.
• [65] L. Vanneschi, M. Castelli, I. Goncalves, L. Manzoni, and S. Silva, ‘Geometric semantic genetic programming for biomedical applications: A state of the art upgrade’, 2017 IEEE Congr. Evol. Comput. CEC 2017 - Proc., ,pp. 177–184, 2017.
• [66] J. P. Papa, G. H. Rosa, and L. P. Papa, ‘A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes’, Pattern Recognit. Lett., vol. 100, ,pp. 59–66, 2017.
• [67] F. Casadei, J. F. B. S. Martins, and G. L. Pappa, ‘A multi-objective approach for symbolic regression with semantic genetic programming’, Proc. - 2019 Brazilian Conf. Intell. Syst. BRACIS 2019, ,pp. 66–71, 2019.
• [68] Z. Qu, Y. X. Chen, L. Liu, Y. Xie, and Q. Zhou, ‘The Algorithm of Concrete Surface Crack Detection Based on the Genetic Programming and Percolation Model’, IEEE Access, vol. 7, ,pp. 57592–57603, 2019.
• [69] Y. Wang, N. Wagner, and J. M. Rondinelli, ‘Symbolic regression in materials science’, MRS Commun., vol. 9, no. 3, ,pp. 793–805, 2019.
• [70] P. DIng, Q. Qian, H. Wang, and J. Yao, ‘A Symbolic Regression Based Residual Useful Life Model for Slewing Bearings’, IEEE Access, vol. 7, ,pp. 72076–72089, 2019.
• [71] G. M. Khan, R. Arshad, and N. M. Khan, ‘Efficient Prediction of Dynamic Tariff in Smart Grid Using CGP Evolved Artificial Neural Networks’, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), vol. 2017-Decem, IEEE,pp. 493–498, 2017.
• [72] J. M. Melo Neto, H. S. Bernardino, and H. J. C. Barbosa, ‘Hybridization of Cartesian Genetic Programming and Differential Evolution for Generating Classifiers Based on Neural Networks’, 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE,pp. 1–8, 2018.
• [73] R. Arshad, G. M. Khan, and S. A. Mahmud, ‘Smart bandwidth management using a recurrent Neuro-Evolutionary technique’, Proc. Int. Jt. Conf. Neural Networks, ,pp. 2240–2247, 2014.
• [74] M. M. Khan, G. M. Khan, and J. F. Miller, ‘Evolution of neural networks using Cartesian Genetic Programming’, IEEE Congress on Evolutionary Computation, IEEE,pp. 1–8, 2010.
• [75] M. Mazari and D. D. Rodriguez, ‘Prediction of pavement roughness using a hybrid gene expression programming-neural network technique’, J. Traffic Transp. Eng. (English Ed., vol. 3, no. 5, ,pp. 448–455, 2016.
• [76] M. Wiglasz and L. Sekanina, ‘Evolutionary approximation of gradient orientation module in HOG-based human detection system’, 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), vol. 2018-Janua, IEEE,pp. 1300–1304, 2017.
• [77] J. Leitner, A. Forster, and J. Schmidhuber, ‘Improving robot vision models for object detection through interaction’, 2014 International Joint Conference on Neural Networks (IJCNN), IEEE,pp. 3355–3362, 2014.
• [78] W. He, S. Hu, S. Li, J. Jin, and K. Li, ‘Human Motion Model Construction Based on Gene Expression Programming’, Springer Singapore,pp. 473–485, 2018.
• [79] W. Fu, M. Johnston, and M. Zhang, ‘Low-level feature extraction for edge detection using genetic programming’, IEEE Trans. Cybern., vol. 44, no. 8, ,pp. 1459–1472, 2014.
• [80] H. Al-Sahaf, M. Zhang, A. Al-Sahaf, and M. Johnston, ‘Keypoints detection and feature extraction: A dynamic genetic programming approach for evolving rotation-invariant texture image descriptors’, IEEE Trans. Evol. Comput., vol. 21, no. 6, ,pp. 825–844, 2017.
• [81] J. Mora, A. Otero, E. De La Torre, and T. Riesgo, ‘Fast and compact evolvable systolic arrays on dynamically reconfigurable FPGAs’, 10th Int. Symp. Reconfigurable Commun. Syst. ReCoSoC 2015, ,pp. 1–7, 2015.
• [82] Z. Vasicek, ‘Bridging the Gap Between Evolvable Hardware and Industry Using Cartesian Genetic Programming’, ,pp. 39–55, 2018.
• [83] Z. Vasicek and L. Sekanina, ‘Evolutionary design of approximate multipliers under different error metrics’, 17th International Symposium on Design and Diagnostics of Electronic Circuits & Systems, IEEE,pp. 135–140, 2014.
• [84] V. Mrazek and Z. Vasicek, ‘Parallel optimization of transistor level circuits using cartesian genetic programming’, Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO ’17, New York, New York, USA,ACM Press,pp. 1849–1856, 2017.
• [85] F. Castejón and E. J. Carmona, ‘Automatic design of analog electronic circuits using grammatical evolution’, Appl. Soft Comput., vol. 62, ,pp. 1003–1018, 2018.
• [86] M. Đumić, D. Šišejković, R. Čorić, and D. Jakobović, ‘Evolving priority rules for resource constrained project scheduling problem with genetic programming’, Futur. Gener. Comput. Syst., vol. 86, ,pp. 211–221, 2018.
• [87] J. Park, Y. Mei, S. Nguyen, G. Chen, and M. Zhang, ‘An investigation of ensemble combination schemes for genetic programming based hyper-heuristic approaches to dynamic job shop scheduling’, Appl. Soft Comput., vol. 63, ,pp. 72–86, 2018.
• [88] M. Đurasević, D. Jakobović, and K. Knežević, ‘Adaptive scheduling on unrelated machines with genetic programming’, Appl. Soft Comput., vol. 48, ,pp. 419–430, 2016.
• [89] M. A. Abud Kappel, ‘Action scheduling optimization using cartesian genetic programming’, Proc. - 2019 Brazilian Conf. Intell. Syst. BRACIS 2019, ,pp. 293–298, 2019.
• [90] T. Dou, Y. Kaszubowski Lopes, P. Rockett, E. A. Hathway, and E. Saber, ‘GPML: an XML-based standard for the interchange of genetic programming trees’, Genet. Program. Evolvable Mach., no. 0123456789, 2019.
• [91] T. Saber, D. Fagan, D. Lynch, S. Kucera, H. Claussen, and M. O’Neill, ‘A Hierarchical Approach to Grammar-Guided Genetic Programming: The Case of Scheduling in Heterogeneous Networks’, ,pp. 225–237, 2018.
• [92] A. Garg, L. Gao, W. Li, S. Singh, X. Peng, X. Cui, Z. Fan, H. Singh, and C. M. M. Chin, ‘Evolutionary framework design in formulation of decision support models for production emissions and net profit of firm: Implications on environmental concerns of supply chains’, J. Clean. Prod., vol. 231, ,pp. 1136–1148, 2019.
• [93] G. Khattak, M. S. Khan, G. M. Khan, F. Huenupan, and M. Curilem, ‘Automatic Classification of seismic signals of the Chilean Llaima Volcano using Cartesian Genetic Programming based Artificial Neural Network’, ,pp. 36 (6 .)-36 (6 .), 2017 .
• [94] K. M. Asim, A. Idris, T. Iqbal, and F. Martínez-Álvarez, ‘Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification’, Soil Dyn. Earthq. Eng., vol. 111, no. February, ,pp. 1–7, 2018.
• [95] T. Zerenner, V. Venema, P. Friederichs, and C. Simmer, ‘Downscaling near-surface atmospheric fields with multi-objective Genetic Programming’, Environ. Model. Softw., vol. 84, ,pp. 85–98, 2016.
• [96] M. Khandelwal, D. J. Armaghani, R. S. Faradonbeh, P. G. Ranjith, and S. Ghoraba, ‘A new model based on gene expression programming to estimate air flow in a single rock joint’, Environ. Earth Sci., vol. 75, no. 9, ,pp. 1–13, 2016.
• [97] K. Zhang, A. Deletic, P. M. Bach, B. Shi, J. M. Hathaway, and D. T. McCarthy, ‘Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach’, J. Environ. Manage., vol. 241, no. January, ,pp. 12–21, 2019.
• [98] A. S. Kima, S. Traore, Y.-M. Wang, and W.-G. Chung, ‘Multi-genes programing and local scale regression for analyzing rice yield response to climate factors using observed and downscaled data in Sahel’, Agric. Water Manag., vol. 146, ,pp. 149–162, 2014.
• [99] J. Ma and G. Teng, ‘A hybrid multiple feature construction approach for classification using Genetic Programming’, Appl. Soft Comput. J., vol. 80, ,pp. 687–699, 2019.
• [100] F. Padillo, J. M. Luna, and S. Ventura, ‘A Grammar-Guided Genetic Programing Algorithm for Associative Classification in Big Data’, Cognit. Comput., vol. 11, no. 3, ,pp. 331–346, 2019.
• [101] O. May Tzuc, I. Hernández-Pérez, E. V. Macias-Melo, A. Bassam, J. Xamán, and B. Cruz, ‘Multi-gene genetic programming for predicting the heat gain of flat naturally ventilated roof using data from outdoor environmental monitoring’, Measurement, vol. 138, ,pp. 106–117, 2019.
• [102] A. Tahmasebi Poor, A. Barari, M. Behnia, and T. Najafi, ‘Determination of the ultimate limit states of shallow foundations using gene expression programming (GEP) approach’, Soils Found., vol. 55, no. 3, ,pp. 650–659, 2015.
• [103] S. K. Beura and P. K. Bhuyan, ‘Operational Analysis of Signalized Street Segments Using Multi-gene Genetic Programming and Functional Network Techniques’, Arab. J. Sci. Eng., vol. 43, no. 10, ,pp. 5365–5386, 2018.
• [104] A. Fallahpour, E. U. Olugu, S. N. Musa, D. Khezrimotlagh, and K. Y. Wong, ‘An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach’, Neural Comput. Appl., vol. 27, no. 3, ,pp. 707–725, 2016.
• [105] H. H. Chen, C. B. Yang, and Y. H. Peng, ‘The trading on the mutual funds by gene expression programming with Sortino ratio’, Appl. Soft Comput. J., vol. 15, ,pp. 219–230, 2014.
• [106] C. H. Lee, C. B. Yang, and H. H. Chen, ‘Taiwan stock investment with gene expression programming’, Procedia Comput. Sci., vol. 35, no. C, ,pp. 137–146, 2014.
• [107] I. Contreras, J. I. Hidalgo, L. Nuñez-Letamendía, and J. M. Velasco, ‘A meta-grammatical evolutionary process for portfolio selection and trading’, Genet. Program. Evolvable Mach., vol. 18, no. 4, ,pp. 411–431, 2017.
• [108] F. Hauser, J. Huber, and B. Kaempff, ‘Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming’, Comput. Econ., vol. 46, no. 2, ,pp. 205–229, 2015.
• [109] H. Qu and X. Li, ‘Building Technical Trading System with Genetic Programming: A New Method to Test the Efficiency of Chinese Stock Markets’, Comput. Econ., vol. 43, no. 3, ,pp. 301–311, 2014.
• [110] B. Ghaddar, N. Sakr, and Y. Asiedu, ‘Spare parts stocking analysis using genetic programming’, Eur. J. Oper. Res., vol. 252, no. 1, ,pp. 136–144, 2016.
• [111] A. Khmag, A. R. Ramli, S. A. R. Al-haddad, S. Yusoff, and N. H. Kamarudin, ‘Denoising of natural images through robust wavelet thresholding and genetic programming’, Vis. Comput., vol. 33, no. 9, ,pp. 1141–1154, 2017.
• [112] S. G. Javed, A. Majid, and Y. S. Lee, ‘Developing a bio-inspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images’, Multimed. Tools Appl., vol. 77, no. 12, ,pp. 15657–15675, 2018.
• [113] M. Yamagiwa, E. Kikuchi, M. Uehara, M. Murakami, and M. Yoneyama, ‘Reconstruction for Artificial Degraded Image Using Constructive Solid Geometry and Strongly Typed Genetic Programming’, 2009 International Conference on Complex, Intelligent and Software Intensive Systems, IEEE,pp. 162–168, 2009.
• [114] P. Thong-On and U. Watchareeruetai, ‘Detection of fibrosis in liver biopsy images using multi-objective genetic programming’, 2017 9th Int. Conf. Inf. Technol. Electr. Eng. ICITEE 2017, vol. 2018-Janua, ,pp. 1–6, 2017.
• [115] M. Iqbal, B. Xue, H. Al-Sahaf, and M. Zhang, ‘Cross-Domain Reuse of Extracted Knowledge in Genetic Programming for Image Classification’, IEEE Trans. Evol. Comput., vol. 21, no. 4, ,pp. 569–587, 2017.
• [116] Í. M. Miranda, C. Aranha, and M. Ladeira, ‘Classification of EEG signals using genetic programming for feature construction’, Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA,ACM,pp. 1275–1283, 2019.
• [117] A. Bhardwaj, A. Tiwari, R. Krishna, and V. Varma, ‘A novel genetic programming approach for epileptic seizure detection’, Comput. Methods Programs Biomed., vol. 124, ,pp. 2–18, 2016.
• [118] M. feli and F. Abdali-Mohammadi, ‘12 Lead Electrocardiography Signals Compression By a New Genetic Programming Based Mathematical Modeling Algorithm’, Biomed. Signal Process. Control, vol. 54, ,p. 101596, 2019.
• [119] N. M. Khan and G. M. Khan, ‘Audio Signal Reconstruction Using Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN)’, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), vol. 2018-Janua, IEEE,pp. 568–573, 2017.
• [120] N. Pillay, ‘The impact of genetic programming in education’, Genet. Program. Evolvable Mach., no. October 2018, 2019.
• [121] W. Xing, R. Guo, E. Petakovic, and S. Goggins, ‘Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory’, Comput. Human Behav., vol. 47, ,pp. 168–181, 2015.
• [122] Y. Li, C. Yuan, C. Zhang, S. Li, K. Sun, and X. Wang, ‘A Novel Approximation Algorithm Based on Genetic Programming in Digital Learning Environment’, 2015 International Conference of Educational Innovation through Technology (EITT), IEEE,pp. 33–36, 2015.
• [123] S. Liu and H. Shi, ‘A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming’, Water Resour. Manag., vol. 33, no. 3, ,pp. 1103–1121, 2019.
• [124] J. Chadalawada, V. Havlicek, and V. Babovic, ‘A Genetic Programming Approach to System Identification of Rainfall-Runoff Models’, Water Resour. Manag., vol. 31, no. 12, ,pp. 3975–3992, 2017.
• [125] A. Danandeh Mehr and V. Nourani, ‘Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling’, Water Resour. Manag., vol. 32, no. 8, ,pp. 2665–2679, 2018.
• [126] A. Aryafar, V. Khosravi, H. Zarepourfard, and R. Rooki, ‘Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran’, Environ. Earth Sci., vol. 78, no. 3, ,pp. 1–13, 2019.
• [127] J. Shiri, A. A. Sadraddini, A. H. Nazemi, O. Kisi, G. Landeras, A. Fakheri Fard, and P. Marti, ‘Generalizability of Gene Expression Programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran’, J. Hydrol., vol. 508, ,pp. 1–11, 2014.
• [128] A. Danandeh Mehr, V. Nourani, E. Kahya, B. Hrnjica, A. M. A. Sattar, and Z. M. Yaseen, ‘Genetic programming in water resources engineering: A state-of-the-art review’, J. Hydrol., vol. 566, no. September, ,pp. 643–667, 2018.
• [129] J. I. Hidalgo, J. M. Colmenar, J. L. Risco-Martin, A. Cuesta-Infante, E. Maqueda, M. Botella, and J. A. Rubio, ‘Modeling glycemia in humans by means of Grammatical Evolution’, Appl. Soft Comput. J., vol. 20, ,pp. 40–53, 2014.
• [130] L. Vanneschi, ‘Improving genetic programming for the prediction of pharmacokinetic parameters’, Memetic Comput., vol. 6, no. 4, ,pp. 255–262, 2014.
• [131] M. B. Erdem, Z. Erdem, and S. Rahnamayan, ‘Diabetes mellitus prediction using multi-objective genetic programming and majority voting’, 14th Int. Conf. Comput. Sci. Educ. ICCSE 2019, no. Iccse, ,pp. 953–958, 2019.
• [132] R. Senatore, A. Della Cioppa, and A. Marcelli, ‘Automatic diagnosis of Parkinson disease through handwriting analysis: A cartesian genetic programming approach’, Proc. - IEEE Symp. Comput. Med. Syst., vol. 2019-June, ,pp. 312–317, 2019.
• [133] D. Moskowitz, ‘Implementing the template method pattern in genetic programming for improved time series prediction’, Genet. Program. Evolvable Mach., vol. 19, no. 1–2, ,pp. 271–299, 2018.
• [134] A. Kattan, S. Fatima, and M. Arif, ‘Time-series event-based prediction: An unsupervised learning framework based on genetic programming’, Inf. Sci. (Ny)., vol. 301, ,pp. 99–123, 2015.
• [135] M. Graff, H. J. Escalante, F. Ornelas-Tellez, and E. S. Tellez, ‘Time series forecasting with genetic programming’, Nat. Comput., vol. 16, no. 1, ,pp. 165–174, 2017.
• [136] L. Yang, J. Zhang, X. Wu, Y. Zhang, and J. Li, ‘A chaotic time series prediction model for speech signal encoding based on genetic programming’, Appl. Soft Comput. J., vol. 38, ,pp. 754–761, 2016.
• [137] F. Yang, M. Li, A. Huang, and J. Li, ‘Forecasting time series with genetic programming based on least square method’, J. Syst. Sci. Complex., vol. 27, no. 1, ,pp. 117–129, 2014.
• [138] B. Barán, J. Paciello, V. Cañete, and N. Hernández, ‘Estimating electrical energy consumption using Linear Genetic Programming’, Proceedings of the The International Conference on Engineering & MIS 2015 - ICEMIS ’15, New York, New York, USA,ACM Press,pp. 1–7, 2015.
• [139] A. Garg, J. S. L. Lam, and L. Gao, ‘Energy conservation in manufacturing operations: Modelling the milling process by a new complexity-based evolutionary approach’, J. Clean. Prod., vol. 108, ,pp. 34–45, 2015.
• [140] I. Boumanchar, K. Charafeddine, Y. Chhiti, F. E. M’hamdi Alaoui, A. Sahibed-dine, F. Bentiss, C. Jama, and M. Bensitel, ‘Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming’, Biomass Convers. Biorefinery, vol. 9, no. 3, ,pp. 499–509, 2019.
• [141] V. H. C. Pinheiro and R. Schirru, ‘Genetic programming applied to the identification of accidents of a PWR nuclear power plant’, Ann. Nucl. Energy, vol. 124, ,pp. 335–341, 2019.
• [142] J. Hu and W. Guo, ‘Flexibility analysis in waste-to-energy systems based on decision rules and gene expression programming’, Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., vol. 2019-Octob, ,pp. 988–993, 2019.
• [143] ‘DEAP’, https://deap.readthedocs.io/en/master/,accessed 6 February 2020.
• [144] ‘GPLEARN’, https://gplearn.readthedocs.io/en/stable/,accessed 6 February 2020.
• [145] ‘Matlab’, https://www.mathworks.com/,accessed 7 February 2020.
• [146] ‘GELAB: A Matlab ToolBox’, https://github.com/adilraja/GELAB,accessed 7 February 2020.
• [147] ‘CGP4Matlab’, https://github.com/tiagoinacio/CGP4Matlab,accessed 7 February 2020.
• [148] R. Miragaia, G. Reis, F. Fernandéz, T. Inácio, and C. Grilo, ‘CGP4Matlab - A Cartesian Genetic Programming MATLAB Toolbox for Audio and Image Processing’, Applications of Evolutionary Computation April 4-6, 2018, Parma, Italy,Springer, Cham,pp. 455–471, 2018.
• [149] ‘GPTIPS 2’, https://sites.google.com/site/gptips4matlab/home,accessed 7 February 2020.
• [150] I. Pan and S. Das, ‘When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming’, Chaos, Solitons & Fractals, vol. 76, ,pp. 141–155, 2015.
• [151] D. P. Searson, ‘GPTIPS 2: An Open-Source Software Platform for Symbolic Data Mining’, Cham,Springer International Publishing,pp. 551–573, 2015.
• [152] P. K. Muduli, M. R. Das, S. K. Das, and S. Senapati, ‘Lateral Load Capacity of Piles in Clay Using Genetic Programming and Multivariate Adaptive Regression Spline’, Indian Geotech. J., vol. 45, no. 3, ,pp. 349–359, 2015.
• [153] D. P. Searson, D. E. Leahy, and M. J. Willis, ‘Predicting the Toxicity of Chemical Compounds Using GPTIPS: A Free Genetic Programming Toolbox for MATLAB’, ,pp. 83–93, 2011.
• [154] ‘PonyGE2’, https://github.com/PonyGE/PonyGE2/,accessed 7 February 2020.
• [155] M. Fenton, J. McDermott, D. Fagan, S. Forstenlechner, E. Hemberg, and M. O’Neill, ‘PonyGE2: grammatical evolution in Python’, Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO ’17, New York, New York, USA,ACM Press,pp. 1194–1201, 2017.
• [156] ‘GeneXproTools’, https://www.gepsoft.com/,accessed 7 February 2020.
• [157] Y. Sun, Z. Li, C. Tang, W. Zhou, and R. Jiang, ‘An Evolving Neural Network for Authentic Emotion Classification’, 2009 Fifth International Conference on Natural Computation, vol. 2, IEEE,pp. 109–113, 2009.
• [158] S. Mehdizadeh, F. Fathian, M. J. S. Safari, and A. Khosravi, ‘Developing novel hybrid models for estimation of daily soil temperature at various depths’, Soil Tillage Res., vol. 197, ,p. 104513, 2020.
• [159] ‘GEP4J’, https://code.google.com/p/gep4j/,accessed 7 February 2020.
• [160] ‘PyGEP’, https://code.google.com/p/pygep/.,accessed 7 February 2020.
• [161] ‘JGEP’, https://sourceforge.net/projects/jgep/.,accessed 7 February 2020.
• [162] ‘Epochx’, https://www.epochx.org/,accessed 7 February 2020.
• [163] ‘Karoo GP’, http://kstaats.github.io/karoo_gp/,accessed 7 February 2020.
• [164] ‘Tensorflow’, https://www.tensorflow.org/,accessed 7 February 2020.
• [165] ‘GISMO’, http://www0.cs.ucl.ac.uk/staff/W.Langdon/gismo/,accessed 8 February 2020.
• [166] W. B. Langdon, ‘Genetic Improvement of Software for Multiple Objectives’, ,pp. 12–28, 2015.
• [167] W. B. Langdon and M. Harman, ‘Optimizing existing software with genetic programming’, IEEE Trans. Evol. Comput., vol. 19, no. 1, ,pp. 118–135, 2015 .
• [168] ‘KNIME’, https://www.knime.com/,accessed 8 February 2020.
• [169] M. R. Berthold, N. Cebron, F. Dill, T. R. Gabriel, T. Kötter, T. Meinl, P. Ohl, K. Thiel, and B. Wiswedel, ‘KNIME - the Konstanz information miner’, ACM SIGKDD Explor. Newsl., vol. 11, no. 1, ,pp. 26–31, 2009 .
• [170] S. O’Hagan and D. B. Kell, ‘Software review: the KNIME workflow environment and its applications in genetic programming and machine learning’, Genet. Program. Evolvable Mach., vol. 16, no. 3, ,pp. 387–391, 2015 .
• [171] ‘GSGP-C++ 2.0’, http://gsgp.sourceforge.net/,accessed 8 February 2020.
• [172] M. Castelli, S. Silva, and L. Vanneschi, ‘A C++ framework for geometric semantic genetic programming’, Genet. Program. Evolvable Mach., vol. 16, no. 1, ,pp. 73–81, 2015 .
• [173] M. Castelli and L. Manzoni, ‘GSGP-C++ 2.0: A geometric semantic genetic programming framework’, SoftwareX, vol. 10, ,p. 100313, 2019.
• [174] ‘GPLAB’, http://gplab.sourceforge.net/,accessed 8 February 2020.
• [175] S. Silva and J. Almeida, ‘GPLAB-a genetic programming toolbox for MATLAB’, Proc. Nord. MATLAB Conf., ,pp. 273–278, 2003 .
• [176] V. K. Dabhi and S. Chaudhary, ‘Empirical modeling using genetic programming: a survey of issues and approaches’, Nat. Comput., vol. 14, no. 2, ,pp. 303–330, 2015.
• [177] W. Banzhaf, ‘Genetic programming and emergence’, Genet. Program. Evolvable Mach., vol. 15, no. 1, ,pp. 63–73, 2014 .
• [178] L. Vanneschi, M. Castelli, and S. Silva, ‘A survey of semantic methods in genetic programming’, Genet. Program. Evolvable Mach., vol. 15, no. 2, ,pp. 195–214, 2014 .
• [179] I. Costa-Carrapiço, R. Raslan, and J. N. González, ‘A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency’, Energy Build., vol. 210, ,p. 109690, 2020.
• [180] Q. Zhang, K. Barri, P. Jiao, H. Salehi, and A. H. Alavi, ‘Genetic programming in civil engineering: advent, applications and future trends’, Artif. Intell. Rev., 2020.
• [181] J. Zhong, L. Feng, and Y.-S. Ong, ‘Gene Expression Programming: A Survey’, IEEE Comput. Intell. Mag., vol. 12, no. 3, ,pp. 54–72, 2017.