Papers using MEP

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If your paper is not here, please add it by filling this Google Form or send it to us by email. Only papers using MEP in the numerical experiments are accepted.

Last update: 2023.12.07.0

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2002

  1. Oltean Mihai, Dumitrescu D., Multi Expression Programming, Technical report, Babeș-Bolyai University, 2002.

2003

  1. Oltean Mihai, Solving Even-Parity Problems using Multi Expression Programming, Proceedings of the 5th International Workshop on Frontiers in Evolutionary Algorithms, The 7th Joint Conference on Information Sciences, September 26-30, 2003, Research Triangle Park, North Carolina, Edited by Ken Chen (et. al), pp. 315-318, 2003.
  2. Oltean Mihai, Groșan C., Evolving Evolutionary Algorithms using Multi Expression Programming, The 7th European Conference on Artificial Life, September 14-17, 2003, Dortmund, Edited by W. Banzhaf (et al), LNAI 2801, pp. 651-658, Springer-Verlag, Berlin, 2003.
  3. András J., Dumitrescu D., Evolving orthogonal decision trees, Studia Universitatis Babeș-Bolyai Informatica, Vol. 48 (2), pp. 33-44, 2003.

2004

  1. Oltean Mihai (et al.), Evolving Digital Circuits for the Knapsack Problem, International Conference on Computational Sciences, E-HARD Workshop, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol. III, pp. 1257-1264, 6-9 June, Krakow, Poland, Springer-Verlag, Berlin, 2004.
  2. Oltean Mihai, Evolving Winning Strategies for Nim-like Games, World Computer Congress, Student Forum, 26-29 August, Toulouse, France, edited by Mohamed Kaaniche, pp. 353-364, Kluwer Academic Publisher, 2004.
  3. Oltean Mihai, Dumitrescu, D., Evolving TSP Heuristics using Multi Expression Programming, International Conference on Computational Sciences, ICCS'04, Edited by M. Bubak, G. D. van Albada, P. Sloot, and J. Dongarra, Vol. II, pp. 670-673, 6-9 June, Krakow, Poland, Springer-Verlag, Berlin, 2004.
  4. Oltean Mihai, Improving the Search by Encoding Multiple Solutions in a Chromosome, in Evolutionary Machine Design, Nova Science Publisher, New-York, edited by Nadia Nedjah (et al.), pp. 81-107, 2004.
  5. Oltean Mihai, Improving Multi Expression Programming: an Ascending Trail from Sea-level Even-3-parity Problem to Alpine Even-18-Parity Problem, chapter 10, Evolvable Machines: Theory and Applications, Springer-Verlag, edited by Nadia Nedjah (et al.), pp. 229-255, 2004.
  6. Oltean Mihai, Groșan C., Evolving Digital Circuits using Multi Expression Programming, NASA/DoD Conference on Evolvable Hardware, 24-26 June, Seattle, Edited by R. Zebulum (et. al), pp. 87-90, IEEE Press, NJ, 2004.
  7. Charnecki Timothy A., Automatic program generation based on the swarm, MSc thesis, Utah State University, ProQuest Dissertations Publishing, 2004.

2005

  1. Oltean Mihai, Evolving reversible circuits for the even-parity problem, EvoHOT workshop, Lausanne, Switzerland, Applications of Evolutionary Computing, Rothlauf, F.; Branke, J.; Cagnoni, S.; Corne, D.W.; Drechsler, R.; Jin, Y.; Machado, P.; Marchiori, E.; Romero, J.; Smith, G.D.; Squillero, G. (Eds.), LNCS 3449, pp. 225-234, Springer-Verlag, Berlin, 2005.
  2. Abraham A., Groșan C., Genetic Programming Approach for Fault Modeling of Electronic Hardware, 2005 IEEE Congress on Evolutionary Computation (CEC'05), Edinburgh, UK, ISBN 0-7803-9363-5, pp. 1563-1569, IEEE Press, 2005.
  3. Groșan C., Abraham A., Ensemble of Genetic Programming Models for Designing Reactive Power Controllers, 5th International Conference on Hybrid Intelligent Systems (HIS'05), Brazil, p. 6, IEEE CS Press, 2005.
  4. Groșan C., Abraham A., Han S.Y., Ramos V., Stock Market Prediction Using Multi Expression Programming, 12th Portuguese Conference on Artificial Intelligence (EPIA'05), Portugal, IEEE Computer Society Press, 2005.
  5. Groșan C., Abraham,A., Han S.Y., MEPIDS: Multi-Expression Programming for Intrusion Detection System, International Work-conference on the Interplay between Natural and Artificial Computation, (IWINAC'05), Spain, Lecture Notes in Computer Science, LNCS 3562, J. Mira and J.R. Alvarez (Eds.), Springer Verlag, Germany, pp. 163-172, 2005.
  6. Fanea A., Dioșan L., Designing a Component-Based Machine Using Multi Expression Programming, Proceeding of the Symposium Colocviul Academic Clujean de Informatica, pp. 93-98, Cluj-Napoca, 2005.
  7. Abraham A., Groșan C., Tran C., Jain L., A CONCURRENT NEURAL NETWORK - GENETIC PROGRAMMING MODEL FOR DECISION SUPPORT SYSTEMS, Knowledge Management, World Scientific, pp. 231-245, 2005.
  8. Groșan C., Abraham A., Solving No Free lunch Issues from a Practical Perspective, 9th International Conference on Cognitive and Neural Systems, ICCNS05, Boston University Press, USA, 2005.

2006

  1. Oltean Mihai, Multi Expression Programming - an in-depth description, Technical Report, Babeș-Bolyai University, 2006.
  2. Groșan C., Abraham A., Stock Market Modeling Using Genetic Programming Ensembles, Genetic Systems Programming, Nadia Nedjah et al. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag, Germany, pp. 131-146, 2006.
  3. Abraham A., Groșan C., Evolving Intrusion Detection Systems, Genetic Systems Programming, Nadia Nedjah et al. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag, Germany, pp. 57-80, 2006.
  4. Abraham A., Groșan C., Decision Support Systems Using Ensemble Genetic Programming, Journal of Information Knowledge Management, World Scientific Publishing Co., Vol. 5, No. 4, pp. 303-313, 2006.
  5. Abraham A., Groșan C., Automatic Programming Methodologies for Electronic Hardware Fault Monitoring, Journal of Universal Computer Science, Vol. 12, No. 4, pp. 408-431, 2006.
  6. Oltean Mihai, A-Brain: the multiple problems solver, ACM Symposium on Applied Computing, pp. 955-959, ACM Press, 2006.
  7. Braunstein, J., Hyeong-Seok Kim, Sungtek Kahng, Sang-Hoon Ha, A Multi-Expression Programming Application to the Design of Planar Antennae, 12th Biennial IEEE Conference on Electromagnetic Field Computation, pp. 123-123, 2006
  8. Wang Zongyue, Application of MEP in image registration, Wuhan University of Technology, 2006.
  9. Groșan C., Abraham A., Evolving computer programs for knowledge discovery, International Journal of System Management, ISNN 0972-6896, Vol. 4(2), pp. 7-24, 2006.

2007

  1. Oltean Mihai, Evolving Evolutionary Algorithms with Patterns, Soft Computing, Springer-Verlag Vol. 11, Issue 6, pp. 503-518, 2007.
  2. Abraham A., Groșan C., Martin-Vide C., Evolutionary Design of Intrusion Detection Programs, International Journal of Network Security, Vol. 4, No. 3, pp. 328-339, 2007.
  3. BAYKASOGLU Adil, OZBAKIR Lale, MEPAR-miner : Multi-expression programming for classification rule mining, European journal of operational research, Vol. 183, Issue 2, pp. 767-784, 2007.
  4. Fatima Zohra Hadjam, Claudio Moraga, Mohamed Benmohamed, Cluster-based evolutionary design of digital circuits using all improved multi-expression programming, Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2007), pp. 2475-2482, ACM Press, 7-11 July 2007.
  5. Fatima Zohra Hadjam, Claudio Moraga, Mustapha Kamel Rahmouni, On the Impact of Migration Parameters on DIMEP for Designing Combinational Circuits, SCCC, pp. 73-82, IEEE Computer Society, 2007
  6. Oltean Mihai, A-Brain: a general system for solving data analysis problems, Journal of Experimental and Theoretical Artificial Intelligence, Vol. 19, pp. 333-353, Taylor & Francis, 2007.
  7. Hadjam F.Z., Moraga C., Rahmouni M.K., Evolutionary design of digital circuits using improved multi expression programming (IMEP), Mathware & Soft Computing, 2007, Vol. 14, Nr. 2, pp. 103-123. ISSN: 1134-5632, 2007.
  8. Wang Z., Ma H., Zhang J., Huang Z., Wu J., Xu H., Image registration method based on evolutionary modeling with constraints, International Symposium on Multispectral Image Processing and Pattern Recognition, International Society for Optics and Photonics, pp. 678630-678630, 2007.
  9. Muntean O., Dioșan L., Oltean Mihai, Solving the even-n-parity problems using Best Sub Tree Genetic Programming, Adaptive Hardware Systems 2007, pp. 511-518, IEEE Press, 2007.
  10. Muntean O., Dioșan L., Oltean Mihai, Best SubTree Genetic Programming, GECCO 2007, pp. 1667 - 1673, ACM Press, 2007.
  11. M. Burak TELCİOĞLU, A NEW GENETIC PROGRAMMING BASED CLASSIFICATION APPROACH IN DATA MINING AND APPLICATION, M.Sc. Thesis, Erciyes University, Graduate School of Natural and Applied Sciences, 2007.
  12. Zhou Guoqiang, Mathematical Modeling and Numerical Simulation of Complex Surface Based on MEP Algorithm, 2007

2008

  1. A.A.R. Heshmati, H. Salehzade, A.H. Alavi, A.H. Gandomi, M. Mohammad Abadi, A Multi Expression Programming Application to High Performance Concrete, World Applied Sciences Journal, Vol. 5 (2), pp. 215-223, 2008.
  2. Adil Baykasoglu, Hamza Gullu, Hanifi Canakci, Lale Ozbakir, Prediction of compressive and tensile strength of limestone via genetic programming, Expert Systems with Applications, Vol. 35, Issues 1-2, pp. 111-123, 2008.
  3. Emel Kizilkaya Aydogan, Cevriye Gence, Mining classification rules with Reduced MEPAR-miner Algorithm, Applied Mathematics and Computation, Vol. 195, Issue 2, pp. 786-798, 2008.
  4. Yuehui Chen, Guangfeng Jia, Liming Xiu, Design of flexible neural trees using multi expression programming, Chinese Control and Decision Conference, IEEE Press, pp. 1429-1434, 2008.
  5. G.V.S.N.R.V. Prasad, Y.Dhanalakshmi, V. Vijaya Kumar, I. Ramesh Babu, Modeling An Intrusion Detection System Using Data Mining And Genetic Algorithms Based On Fuzzy Logic, International Journal of Computer Science and Network Security, Vol. 8 No. 7, pp. 319-325, 2008.
  6. Guangfeng Jia, Yuehui Chen, Qiang Wu, A MEP and IP Based Flexible Neural Tree Model for Exchange Rate Forecasting, 4th International Conference on Natural Computation, ICNC '08., Vol. 5, pp. 299-303, 2008.
  7. Liu Mingjun, Xiu Liming, Jia Guangfeng, Chen Yuehui, Crack Fault Diagnosis Based on MEP Based Neural Network, International Symposium on Computer Science and Computational Technology, ISCSCT '08., pp. 635 - 639, IEEE, 2008.
  8. Guangfeng Jia, Yuehui Chen, Peng Wu, MENN Method Applications for Stock Market Forecasting, 5th International Symposium on Neural Networks, pp. 30-39, LNCS 5263, 2008.
  9. Jia Guang-feng, Ren Ai-hua, Wu Qiang, Chen, Yue-hui, Exchange Rate Forecasting Using Multi-Expression Programming, Journal of University of Jinan(Science and Technology), Vol. 1, 2008.
  10. John M. Palmer, Relative Referenced Genetic Programming, Complex Systems, Vol. 17, pp. 339–356, 2008.
  11. Yorick Hardy, Kiat Shi Tan, Willi-Hans Steeb, Computer Algebra with Symbolic C++, World Scientific, 2008.
  12. Dai, Shucheng, Changjie Tang, Mingfang Zhu, Yu Chen, Peng Chen, Shaojie Qiao, Chuan Li, MERGE: A Novel Evolutionary Algorithm Based on Multi Expression Gene Programming, 4th International Conference on Natural Computation, pp. 320-324, IEEE, 2008.

2009

  1. Oltean Mihai, Groșan C., Dioșan L., Mihailă C., Genetic Programming with linear representation - a survey, International Journal on Artificial Intelligence Tools (IJAIT), Vol. 18, pp. 197-238, World Scientific, 2009.
  2. Shixiong Xia, Zuhui Hu, Qiang Niu, An Approach of Semantic Similarity Measure between Ontology Concepts Based on Multi Expression Programming, 6th Web Information Systems and Applications Conference, pp. 184-188, IEEE Press, 2009.
  3. Wang YaNan, Yang Bo, Zhao Xiuyang, Contour Registration Based on Multi-Expression Programming and the Improved ICP, International Symposium on Computer Network and Multimedia Technology, IEEE Press, pp. 1-4, 2009.
  4. Abraham A., Groșan C., Vaclav Snasel, Programming Risk Assessment Models for Online Security Evaluation Systems, UKSIM, International Conference on Computer Modeling and Simulation, pp. 41-46, IEEE Press, 2009.
  5. Bin Yang, Yuehui Chen, Qingfang Meng, Inference of Differential Equations for Modeling Chemical Reactions, 6th International Symposium on Neural Networks, LNCS 5551, pp. 1014-1023, 2009.
  6. Mark Neville Richard Smith, An Investigation Of Meta-Evolution Using Multi-Expression Genetic Programming, Master of Science Degree in Software Development, Open University, 2009.
  7. Yang B., Chen Y., Meng Q., Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks, Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, Huang DS., Jo KH., Lee HH., Kang HJ., Bevilacqua V. (eds), pp. 974-983, LNCS 5755, 2009
  8. Jiang Dazhi, Research and Application of Programming Method of Linear Gene Coding, 2009.
  9. Hu Zu-hui, Xia Shi-xiong, Niu Qiang, Application of improved multi expression programming algorithm in function finding problem, Computer Engineering and Design, 2009.
  10. Yuehui Chen, Bin Yang, Yaou Zhao, Qingfang Meng, Evolving Additive Trees for Modeling Biochemical Systems, The Third International Symposium on Optimization and Systems Biology (OSB'09), Zhangjiajie, China, ORSC & APORC, pp. 132–141, 2009.
  11. Liu Ming-jun, Xiu Li-ming, Li Jin-ping, Peng Xi-yuan, Crack fault diagnosis based on MEP and frequency contour lines, Journal of Shandong University (Natural Science), 2009.
  12. Banković Z., Bojanić S., Nieto-Taladriz O., Evaluating Sequential Combination of Two Genetic Algorithm-Based Solutions for Intrusion Detection, In: Corchado E., Zunino R., Gastaldo P., Herrero Á. (eds), International Workshop on Computational Intelligence in Security for Information Systems CISIS’08. Advances in Soft Computing, Vol. 53, pp. 147-154, Springer, 2009.
  13. Willi-Hans Steeb, Yorick Hardy, Problems and Solutions for Bit and String Manipulations, 2009.
  14. Oltean Mihai, Muntean O., Evolutionary design of graph-based structures for optical computing, 2nd workshop on Optical SuperComputing, OSC 2009, pp. 56–69, LNCS 5882, Springer-Verlag, 2009.

2010

  1. Alavi A.H., Gandomi A.H., Sahab M.G., Gandomi M., Multi Expression Programming: A New Approach to Formulation of Soil Classification, Engineering with Computers, Springer, 26(2): 111-118, 2010.
  2. Shahnazari H., Dehnavi Y., Alavi A.H., Numerical Modeling of Stress-Strain Behavior of Sand under Cyclic Loading, Engineering Geology, Elsevier, 116(1-2): 53-72, 2010.
  3. Cattani, P.T., Johnson, C.G., ME-CGP: Multi Expression Cartesian Genetic Programming, IEEE Congress on Evolutionary Computation, pp. 1-6, 2010.
  4. Mousavi, S.M. Alavi, A.H. Gandomi, A.H., Esmaeili, M. Arab, Gandomi, M., A data mining approach to compressive strength of CFRP-confined concrete cylinders, Structural Engineering and Mechanics, Vol. 36, Issue 6, pp. 759-783, Techno-Press, 2010.
  5. Fatima Zohra Hadjam, Claudio Moraga, Evolutionary design of reversible digital circuits using IMEP the case of the even parity problem, Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2010, IEEE Press, pp. 1-6, 2010.
  6. Baykasoğlu, A., Göçken, M. and Özbakir, L., Genetic Programming Based Data Mining Approach to Dispatching Rule Selection in a Simulated Job Shop, SIMULATION, 86(12), pp. 715-728, 2010.
  7. Zhang Jian-wei, Wu Zhi-jian, Huang Zhang-can, Jiang Da-zhi, Guo Jing-lei, Classification Based on Multi Expression Programming, Journal of Chinese Computer Systems, pp.125-128, 2010.
  8. Shen Xueli, Zhang Hongyan, Huang Xiu, Research on application of neural tree in prediction of coal and gas outburst, WORLD SCI-TECH R&D, Vol. 32, Issue 3, pp. 303-306, 2010.
  9. Dazhi Jiang, Zhifei Wang, Haojun Sun, Yulin Du, A unified fitness calculation method for automatic modeling algorithms, 8th World Congress on Intelligent Control and Automation, pp. 1569-1573, 2010.
  10. Deng Wei, The fusion of GEP and MEP together with the novel decoding evaluation technology, Master Thesis, Changsha University of Science and Technology, 2010.
  11. Zhang Jianwei, Wu Zhijian, Huang Zhangcan, Jiang Dazhi, Guo Jinglei, Research on Classification Algorithm Based on Multi-Expression Programming, Small Microcomputer System, Issue 7, pp. 1371-1374, 2010.

2011

  1. Alavi A.H., Gandomi A.H., Modaresnezhad M., Mousavi M., New Ground-Motion Prediction Equations Using Multi Expression Programming, Journal of Earthquake Engineering, Taylor & Francis, 15(4): 511-536, 2011.
  2. Gandomi A.H., Alavi A.H., Yun G.J., Formulation of Uplift Capacity of Suction Caissons Using Multi Expression Programming, KSCE Journal of Civil Engineering, Springer, Vol. 15, Issue 2, pp. 363-373, 2011.
  3. Alavi A.H., Gandomi A.H., A Robust Data Mining Approach for Formulation of Geotechnical Engineering Systems, International Journal for Computer-Aided Engineering and Software-Engineering Computations, Emerald, Vol. 28, Issue 3, pp. 242-274, 2011.
  4. W-H Steeb, The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithm, Gene Expression Programming, Wavelets, Fuzzy Logic with C++, Java and Symbolic C++ Programs, World Scientific Publishing, ISBN 978-981-4335-77-5, 2011.
  5. Qu Shou-ning, Wang Wen-na, Multi-expression programming for structure optimization algorithm on FNT in the application of process object modelling, International Journal on Advances in Information Sciences and Service Sciences, pp. 61-69, 2011.
  6. Yuttana Suttasupa, Suppat Rungraungsilp, Suwat Pinyopan, Pravit Wungchusunti, Prabhas Chongstitvatana, A Comparative Study of Linear Encoding in Genetic Programming, 9th International Conference on ICT and Knowledge, IEEE Press, pp. 13-17, 2011.
  7. Mohammad Reza Mirzahosseini, Alireza Aghaeifar, Amir Hossein Alavi, Amir Hossein Gandomi, Reza Seyednour, Permanent deformation analysis of asphalt mixtures using soft computing techniques, Expert Systems with Applications, Vol. 38, Issue 5, pp. 6081-6100, 2011.
  8. Xiuyang Zhao, Caiming Zhang, Yanan Wang, Bo Yang, A hybrid approach based on MEP and CSP for contour registration, Applied Soft Computing Vol. 11, Issue 8, pp. 5391-5399, December 2011.
  9. Lale ÖZBAKIR, Adil BAYKASOĞLU, GENETĠK PROGRAMLAMAYA DAYALI SINIFLANDIRMA YAKLAġIMI: MEPAR-MINER, Endüstri Mühendisliği Yazılımları ve Uygulamaları Kongresi, pp. 211-222, 2011.
  10. Jiang Da-zhi, Peng Chen-feng, Liu Liang, Forecasting Soil Slope Stability with MEP Algorithm, Journal of Shantou University, Vol. 26, pp. 66-72, 2011.
  11. Janeczek Michał, Comparison of Gene Expression Programming with other genetic algorithms, Master thesis, 2011.
  12. Zhu Ming-fang, Ren Yan-ling, Zhang Jian-bin, Study on function finding problem based on multi-expression programming, Computer Engineering and Design, Issue 3, pp. 1134-1137, 2011.
  13. Amir Hossein Gandomi, Amir Hossein Alavi, Multi-stage genetic programming: A new strategy to nonlinear system modeling, Information Sciences, Vol. 181, Issue 23, pp. 5227-5239, 2011.
  14. Zhou Kaiqing, Le Xiaobo, Pan Xiaohai, Mo Liping, Linear Genetic Programming Algorithm Based on Cellular Automata, Computer Engineering, Vol. 16, pp. 161-163, 2011.

2012

  1. Alavi A.H., Mollahasani A., Gandomi A.H., Bolouri Bazaz J., Formulation of Secant and Reloading Soil Deformation Moduli Using Multi Expression Programming, International Journal for Computer-Aided Engineering and Software-Engineering Computations, Emerald, Vol. 29 Iss: 2, pp.173 - 197, 2012.
  2. Wei Deng, Pei He, Improving Multi Expression Programming Using Reuse-Based Evaluation, 6th International Symposium, ISICA 2012, Computational Intelligence and Intelligent Systems Communications in Computer and Information Science, pp. 292-299, 2012.
  3. Amir Hossein Alavi, Amir Hossein Gandomi, Energy-based numerical models for assessment of soil liquefaction, Geoscience Frontiers Vol. 3, Issue 4, pp. 541-555, 2012.
  4. Mehdi Divsalar, Habib Roodsaz, Farshad Vahdatinia, Ghassem Norouzzadeh, Amir Hossein Behrooz, A Robust Data-Mining Approach to Bankruptcy Prediction, Journal of Forecasting, Vol. 31, Issue 6, pp. 504-523, 2012.
  5. Qingke Zhang, Bo Yang, Lin Wang, Fuxiang Zhu, Predicting Cement Compressive Strength Using Double-Layer Multi-expression Programming, 4th International Conference on Computational and Information Sciences (ICCIS), pp. 94-97, IEEE Press, 2012.
  6. W. Wang, J. Cai, S. Qu, Process object overall modeling based on multi-expression programming for structure optimization algorithm, 2012.
  7. Yaghouby F., Ayatollahi A., Bahramali R., Yaghouby M., Robust genetic programming-based detection of atrial fibrillation using RR intervals, Expert Systems, Vol. 29, pp. 183-199, 2012.
  8. A. A. Heshmati R, M. Mokhtari, E. Afshari, Simulation of Shear Strength of Soils Using a Hybrid MEP and PSO Method, The 4th International Conference on Computational Methods (ICCM2012), 2012.

2013

  1. Alavi A.H., Gandomi A.H., Chahkandi Nejad H., Mollahasani A., Rashed A. Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems, Neural Computing & Applications, Springer, 2013. DOI: 10.1007/s00521-012-1144-6
  2. Gandomi A.H., Alavi A.H., Expression Programming Techniques for Formulation of Structural Engineering Systems, Metaheuristic Applications in Structures and Infrastructures, AH Gandomi et al. (Eds.), Elsevier, Chapter 18, 437-454, 2013.
  3. Mehdi Bagheri, Amir Hossein Gandomi, Mehrdad Bagheri, Mohcen Shahbaznezhad, Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials, Expert Systems Vol. 30, Issue 1, pp. 66-78, February 2013.
  4. Wei Deng, Pei He, Zhi Huang, Multi-Expression Based Gene Expression Programming, Proceedings of 2013 Chinese Intelligent Automation Conference, Lecture Notes in Electrical Engineering, Vol. 256, pp. 439-448, Springer, 2013.
  5. Long Bin Chen, Pei He, Multi-Subexpression Programming, Applied Mechanics and Materials, Vols. 411-414, pp. 2067-2073, Trans Tech Publications, 2013.
  6. Weihong Wang, Wenrou Lin, Qu Li, Image retrieval based on Multi Expression Programming algorithms, 9th International Conference on Natural Computation (ICNC), pp. 1359 - 1364, 2013.
  7. Yu Wang, Study on Improved Flexible Neural tree Optimization Algorithm, Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA-13), Advanced Materials Research, Vols. 765-767, pp. 1055-1059, Sep. 2013
  8. Zhang Qingke, Bo Yang, Lin Wang, Jianzhang Jiang, An improved multi-expression programming algorithm applied in function discovery and data prediction, International Journal of Information and Communication Technology, Vol. 5, No. 3-4, pp. 218-233, 2013.
  9. Bo Yang, Qingke Zhang, Lin Wang, Yi Li, Inference of differential equations by M-MEP for cement hydration modeling, 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE Press, pp. 4-10, 2013
  10. Andreica A., Dioșan L., Găceanu R., Sîrbu A., PEDESTRIAN RECOGNITION BY USING KERNEL DESCRIPTORS, Studia Universitatis Babeș-Bolyai Informatica, Vol. 58, Issue 2, pp. 77-89, 2013.
  11. Amir Hossein Gandomi, Xin-She Yang, Siamak Talatahari, Amir Hossein Alavi, Metaheuristic Algorithms in Modeling and Optimization, Metaheuristic Applications in Structures and Infrastructures, Elsevier, pp. 1-24, 2013.

2014

  1. Mohammadzadeh D., Bolouri Bazaz J., Alavi A.H., An Evolutionary Computational Approach for Formulation of Compression Index of Fine-Grained Soils, Engineering Applications of Artificial Intelligence, Elsevier, 33: 58-68, 2014.
  2. Fatima Hadjam, Claudio Moraga, Introduction to RIMEP2: A Multi-Expression Programming System for the Design of Reversible Digital Circuits, arXiv:1405.2226v2
  3. Fatima Zohra Hadjam, Claudio Moraga, RIMEP2: Evolutionary Design of Reversible Digital Circuits, ACM Journal on Emerging Technologies in Computing Systems, Vol. 11, Issue 3, 23 pages, 2014.
  4. Najla Akram Al-Saati, Taghreed Riyad Jarallah, Software Effort Estimation Using Multi Expression Programming, AL-Rafidain Journal of Computer Sciences and Mathematics, Vol. 11, Issue 2, pp. 53-71, 2014
  5. Cattani Philip Thomas, Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification, PhD Thesis, University of Kent, 2014.
  6. Mwaura J., Keedwell E., On using Gene Expression Programming to evolve multiple output robot controllers, IEEE International Conference on Evolvable Systems, pp. 173-180, 2014.
  7. Alireza Mohammadi Bayazidi, Gai-Ge Wang, Hamed Bolandi, Amir H. Alavi, Amir H. Gandomi, Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete, Mathematical Problems in Engineering, vol. 2014, Article ID 474289, 10 pages, 2014.
  8. Zheng Qiusheng, Research on semantic reuse of MEP and GEP, Master thesis, Changsha University of Science and Technology, 2014.

2015

  1. Dazhi Jiang, Zhun Fan, The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators, Mathematical Problems in Engineering, 2015.
  2. Amir H. Gandomi, Ali Faramarzifar, Peyman Ghanad Rezaee, Abazar Asghari, Siamak Talatahari, New design equations for elastic modulus of concrete using multi expression programming, Journal of Civil Engineering and Management, Vol. 21, Issue 6, pp. 761-774, 2015.
  3. Dioșan L., Andreica A., Multi-objective breast cancer classification by using multi-expression programming, Applied Intelligence, Vol. 43, pp. 499–511, Springer, 2015.
  4. Hadjam Fatima Z., Tuning of Parameters of a Soft Computing System for the Synthesis of Reversible Circuits, Journal of Multiple-Valued Logic & Soft Computing, Vol. 24, Issue 1-4, pp. 341-368, 2015.
  5. ZHENG Qiu sheng1, HE Pei, LI Ji, New evaluation method for multiple expression programming design, Computer Engineering & Science, Vol. 37, Issue 02, pp. 314-319, 2015.
  6. Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura, A Classification Module for Genetic Programming Algorithms in JCLEC, Journal of Machine Learning Research, Vol. 16(15), pp. 491-494, 2015.

2016

  1. Qingke Zhang, Xiangxu Meng, Bo Yang, Weiguo Liu, MREP: Multi-Reference Expression Programming, 12th International Conference on Intelligent Computation, LNCS 9772, pp. 26-38, 2016.
  2. Xin Ma, Zhi-bin Liu, Predicting the Oil Well Production Based on Multi Expression Programming, The Open Petroleum Engineering Journal, Vol. 9, pp. 21-32, 2016.
  3. Danial Mohammadzadeh S., Jafar Bolouri Bazaz, S. H. Vafaee Jani Yazd, Amir H. Alavi, Deriving an intelligent model for soil compression index utilizing multi-gene genetic programming, Environmental Earth Sciences, Vol. 75, Issue 3, pp. 262, 2016.
  4. Kulluk Sinem, Ozbakir Lale, Tapkan Pinar Zarif, Baykasoglu Adil, Cost-sensitive meta-learning classifiers, Knowledge-Based Systems MEPAR-miner and DIFACONN-miner, Vol. 98, Issue C, pp. 148-161, 2016.
  5. Jain Sparsh, Modelling the Deflection of Flexible Pavement using Artificial Intelligence Techniques, MTech thesis, 2016.

2017

  1. Alireza Fallahpour, Ezutah Udoncy Olugu, Siti Nurmaya Musa, A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP), Neural Computing and Applications, Vol. 28, Issue 3, pp. 499-504, Springer, 2017.
  2. Najla Akram Al-Saati, Taghreed Riyadh Alreffaee, Using Multi Expression Programming in Software Effort Estimation, International Journal of Recent Research and Review, Vol. X, Issue 2, 2017.
  3. Sonia Thomas, G.N. Pillai, Kirat Pal, Prediction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm, Geomatics, Natural Hazards and Risk, Vol. 8, Issue 2, pp. 177-193, Taylor & Francis, 2017.
  4. Steven Mandla Masimula, Gene Expression Programming for logic circuit design, Master Thesis, University of South Africa, 2017.
  5. Alain Petrowski, Sana Ben-Hamida, Evolutionary Algorithms, John Wiley & Sons, 2017.

2018

  1. Kerkez Marija, Ralevic Nebojsa M., Todorovic Tanja, Zezelj Boris, Risk assessment based on integrated fuzzy MEP methodology, 30th International Scientific Conference on Economic and Social Development, Belgrade, 25-26 May, 2018.
  2. Chareka, Tatenda Herbert, Network intrusion detection using genetic programming, Masters Degree. University of KwaZulu-Natal, Pietermaritzburg, 2018.
  3. Markus Jesswein, Jinyuan Liu, Minkyung Kwak, Predicting the Side Resistance of Piles Using a Genetic Algorithm and SPT N-Values, GeoEdmonton Conference, 2018.
  4. Pınar Zarif TAPKAN, Tayfun ÖZMEN, Determining the yarn quality by feature selection and classification in a yarn production facility, Pamukkale University Journal of Engineering Sciences, 24(4), pp. 713-719, 2018.
  5. F. A. Abulalqader, A. W. Ali, Comparing Different Estimation Methods for Software Effort, International Conference on Information and Sciences (AiCIS), pp. 13-22, 2018

2019

  1. Masoud Sarveghadi, Amir H. Gandomi, Hamed Bolandi, Amir H. Alavi, Development of prediction models for shear strength of SFRCB using a machine learning approach, Neural Computing and Applications, Vol. 31, pp. 2085–2094, 2015.
  2. Marija PAUNOVIIĆ, Nebojša RALEVIC, Olivera MILUTINOVIĆ, Željko VOJINOVIC, Biljana MLADENOVIĆ-VOJINOVIĆ, Integrated Fuzzy System and Multi-Expression Programming Techniques for Supplier Selection, Technical Gazette, Vol. 26(1), pp. 122-127, 2019.
  3. Bin Yang, Wenzheng Bao, RNDEtree: Regulatory Network With Differential Equation Based on Flexible Neural Tree With Novel Criterion Function, IEEE Access, Vol. 7, pp. 58255-58263, 2019.
  4. Ahmed H. Elbosraty, Ahmed Ebid, Ayman L Fayed, Predicting (Nk) factor of (CPT) test using (GP): Comparative Study of MEPX & GN7, International Journal of Scientific and Engineering Research, Vol. 10(3):613, 2019
  5. A. Anjum, M. Islam, L. Wang, Gene Permutation: A new Probabilistic Genetic Operator for Improving Multi Expression Programming, IEEE Symposium Series on Computational Intelligence (SSCI), pp. 3139-3146, 2019.
  6. Adelia Rosyida, Fitwatur, PERBANDINGAN PERSAMAAN LEAST SQUARE METHOD DAN PERSAMAAN MULTI EXPRESSION PROGRAMMING (MEP) DALAM PREDIKSI PRODUKSI SUMUR MINYAK, Other thesis, Universitas Pembangunan Nasional Veteran Yogyakarta, 2019.
  7. B. Yang, W. Bao, Complex-Valued Ordinary Differential Equation Modeling for Time Series Identification, IEEE Access, Vol. 7, pp. 41033-41042, 2019.
  8. Jinyuan Liu, Markus Jesswein, Genetic Algorithm-Based Load-Settlement Curves of Driven Piles in Glacial Deposits, Geotechnical Engineering in the XXI Century: Lessons learned and future challenges, IOS Press, pp. 1068-1075, 2019.
  9. Pirhadi N, Tang X, Yang Q., Energy Evaluation of Triggering Soil Liquefaction Based on the Response Surface Method, Applied Sciences, Vol. 9, Issue 4, pp.694, 2019.
  10. Araghi M, Khatibinia M., MODELING OF FLOW NUMBER OF ASPHALT MIXTURES USING A MULTI–KERNEL BASED SUPPORT VECTOR MACHINE APPROACH International Journal of Optimization in Civil Engineering, Vol. 9, Issue 2, pp. 233-250, 2019.
  11. Tugas Akhir, PERBANDINGAN PERSAMAAN LEAST SQUARE METHOD DAN PERSAMAAN MULTI EXPRESSION PROGRAMMING (MEP) DALAM PREDIKSI PRODUKSI SUMUR MINYAK, Other thesis, FAKULTAS TEKNIK INDUSTRI UNIVERSITAS PEMBANGUNAN NASIONAL VETERAN, YOGYAKARTA, 2019

2020

  1. Sohrab Sharifi, Saeed Abrishami, Amir H.Gandomi, Consolidation assessment using Multi Expression Programming, Applied Soft Computing, Vol. 86, 105842, Elsevier, 2020.
  2. Han-Lin Wang, Zhen-Yu Yin, High performance prediction of soil compaction parameters using multi expression programming, Engineering Geology, Vol. 276, 105758, Elsevier, 2020.
  3. Alireza Arabshahi, Nima Gharaei-Moghaddam, Mohammadreza Tavakkolizadeh, Development of applicable design models for concrete columns confined with aramid fiber reinforced polymer using Multi-Expression Programming, Structures, Vol. 23, pp. 225-244, Elsevier, 2020.
  4. M. Talebidalouei, Seyed Ahmad Mirbagheri, Pouria Nakhaei, Treatment prediction of sugar industry wastewater in moving-bed biofilm reactor using multi expression programming, Desalination and Water Treatment, Vol. 191, pp. 82-92, 2020.
  5. B. Yang, W. Bao, Complex-Valued Ordinary Differential Equation Modeling for Time Series Identification, IEEE Access, Vol. 7, pp. 41033-41042, IEEE, 2019.
  6. A. Arabshahi, N. Gharaei-Moghaddam, M. Tavakkolizadeh, Ultimate stress and strain models for FRP confined concrete columns with inclined fiber orientation, 12th National Congress on Civil Engineering, 2020.
  7. A. Arabshahi, N. Gharaei-Moghaddam, M. Meghdadian, M. Tavakkolizadeh, Predictive model for confinement pressure of partially FRP confined concrete columns, 12th National Congress on Civil Engineering, 2020.
  8. Mohammad Meghdadian, Nima Gharaei-Moghaddam, Alireza Arabshahi, Navid Mahdavi, Mansour Ghalehnovi, Proposition of an equivalent reduced thickness for composite steel plate shear walls containing an opening, Journal of Constructional Steel Research, Vol. 168, 105985, Elsevier, 2020.
  9. Han-Lin Wang, Zhen-Yu Yin, Pin Zhang, Yin-Fu Jin, Straightforward prediction for air-entry value of compacted soils using machine learning algorithms, Engineering Geology, Vol. 279, 105911, Elsevier, 2020.
  10. Saeid Ghorbani, Sohrab Sharifi, Hamed Rokhsarpour, Sara Shoja, Mostafa Gholizadeh, Mohammad Ali Dashti Rahmatabad, Jorge de Brito, Effect of magnetized mixing water on the fresh and hardened state properties of steel fibre reinforced self-compacting concrete, Construction and Building Materials, Vol. 248, 118660, Elsevier, 2020.
  11. Jimenez, V. A., Lescano, G. E., Will, A. L. E, Regresión Simbólica aplicada a la Predicción del Consumo Eléctrico a Corto Plazo en el Nivel de Subestación, Symbolic Regression applied to the Short-Term Load Forecasting on the Substation level, Revista Tecnología Y Ciencia, Vol. 39, pp. 85–102, 2020.
  12. Luo, X., Yan, X., Chen, Y. et al., The prediction of shale gas well production rate based on grey system theory dynamic model GM(1, N), Journal of Petroleum Exploration and Production Technology, Vol. 10, pp. 3601-3607, Springer, 2020.
  13. Ghanizadeh Ali Reza, Safi Jahanshahi Farzad, Khalifeh Vahid, Jalali Farhang, Predicting Flow Number of Asphalt Mixtures Based on the Marshall Mix design Parameters Using Multivariate Adaptive Regression Spline (MARS), International Journal of Transportation Engineering, Vol. 7, Issue 4, pp. 433-448, 2020.
  14. Lin Shuzhen, Research on the fusion method and architecture of MEP and GEP, Master thesis, Guangzhou University, 2020.

2021

  1. Qianyun Zhang, Kaveh Barri, Pengcheng Jiao, Hadi Salehi, Amir H. Alavi, Genetic programming in civil engineering: advent, applications and future trends. Artificial Intelligence Review, Vol. 54, pp. 1863-1885, Springer, 2021.
  2. Muhammad Farjad Iqbal, Muhammad Faisal Javed, Momina Rauf, Iftikhar Azim, Muhammad Ashraf, Jian Yang, Qing-feng Liu, Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming, Science of The Total Environment, Vol. 780, 146524, Elsevier, 2021.
  3. Alireza Fallahpour, Kuan Yew Wong, Srithar Rajoo, Guangdong Tian, An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming, Journal of Cleaner Production, Vol. 283, 125287, Elsevier, 2021.
  4. Hong-Hu Chu, Mohsin Ali Khan, Muhammad Javed, Adeel Zafar, M. Ijaz Khan, Hisham Alabduljabbar, Sumaira Qayyum, Sustainable use of fly-ash: Use of gene-expression programming (GEP) and multi-expression programming (MEP) for forecasting the compressive strength geopolymer concrete, Ain Shams Engineering Journal, Vol 12. Issue 4, pp. 3603-3617, Elsevier, 2021.
  5. Fazal E. Jalal, Yongfu Xu, Mudassir Iqbal, Babak Jamhiri, Muhammad Faisal Javed, Predicting the compaction characteristics of expansive soils using two genetic programming-based algorithms, Transportation Geotechnics, Vol. 30, 100608, Elsevier, 2021.
  6. Shah, Muhammad I., Muhammad N. Amin, Kaffayatullah Khan, Muhammad S.K. Niazi, Fahid Aslam, Rayed Alyousef, Muhammad F. Javed, and Amir Mosavi, Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete, Sustainability, Vol. 13, No. 5: 2867, MDPI, 2021.
  7. Han-Lin Wang, Zhen-Yu Yin, Unconfined compressive strength of bio-cemented sand: state-of-the-art review and MEP-MC-based model development, Journal of Cleaner Production, Vol. 315, 128205, Elsevier, 2021.
  8. Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed, Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete, Advances in Civil Engineering, Article ID: 6682283, Hindawi, 2021.
  9. Sahar Mahdinia, Mohammadreza Tavakkolizadeh, Proposed a model for compressive strength of cement mortar by using Multi Expression Programing, 12th International Congress on Civil Engineering, 2021.
  10. Alireza Arabshahi, Sima Rostami Aghouy, Nima Gharaei-Moghaddam, Determination of the Effective Moment of Inertia for RC Beams strengthened with FRP Sheets Using Multi Expression Programming, 12th International Congress on Civil Engineering, 2021.
  11. Bahare Farahani, Ali Derakhshani, Settlement prediction of shallow foundations on granular soils using Multi Expression Programming (MEP), Journal of Ferdowsi Civil Engineering, Vol. 34, No. 1, 2021.
  12. Sabour, M.R., Dezvareh, G.A., Niavol, K.P., Application of Artificial Intelligence Methods in Modeling Corrosion of Cement and Sulfur Concrete in Sewer Systems, Environmental Processes, Vol. 8, pp. 1601–1618, Springer, 2021.
  13. Amin, Muhammad N., Kaffayatullah Khan, Fahid Aslam, Muhammad I. Shah, Muhammad F. Javed, Muhammad A. Musarat, and Kseniia Usanova, Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete, Materials, Vol. 14, No. 19: 5659, MDPI, 2021.
  14. Ilyas, Israr, Adeel Zafar, Muhammad F. Javed, Furqan Farooq, Fahid Aslam, Muhammad A. Musarat, Nikolai I. Vatin, Forecasting Strength of CFRP Confined Concrete Using Multi Expression Programming, Materials, Vol. 14, No. 23: 7134, MDPI, 2021.

2022

  1. Kennedy C. Onyelowe, Fazal E. Jalal, Mudassir Iqbal, Zia Ur Rehman, Kizito Ibe, Intelligent modeling of unconfined compressive strength (UCS) of hybrid cement-modified unsaturated soil with nanostructured quarry fines inclusion, Innovative Infrastructure Solutions, Vol. 7, 98, Springer, 2022.
  2. Alireza Arabshahi, Masoumeh Tavakol, Javad Sabzi, Nima Gharaei-Moghaddam, Prediction of the effective moment of inertia for concrete beams reinforced with FRP bars using an evolutionary algorithm, Structures, Vol. 35, pp. 684-705, Elsevier, 2022.
  3. Aso Akram Abdalla, Ahmed Salih Mohammed, Implementation of multi-expression programming (MEP), artificial neural network (ANN), and M5P-tree to forecast the compression strength cement-based mortar modified by calcium hydroxide at different mix proportions and curing ages, Innovative Infrastructure Solutions Vol. 7(2):153, Springer, 2022.
  4. Awan HH, Hussain A, Javed MF, Qiu Y, Alrowais R, Mohamed AM, Fathi D, Alzahrani AM, Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming, Buildings, Vol. 12(3):314, MDPI, 2022.
  5. Yan Zhang, Wenhui Chu, Mahmood Ahmad et al., The Establishment of Prediction Model for Soil Liquefaction Based on the Seismic Energy Using the Neural Network, Environmental Earth Sciences, Vol. 81, 114, Springer, 2022.
  6. Oltean Mihai, Multi Expression Programming for solving classification problems, Technical report, 2022.
  7. Aldrees A, Khan MA, Tariq MAUR, Mustafa Mohamed A, Ng AWM, Bakheit Taha AT, Multi-Expression Programming (MEP): Water Quality Assessment Using Water Quality Indices, Water, Vol. 14(6):947, MDPI, 2022.
  8. Mohsin Ali Khan, Fahid Aslam, Muhammad Faisal Javed, Hisham Alabduljabbar, Ahmed Farouk Deifalla, New prediction models for the compressive strength and dry-thermal conductivity of bio-composites using novel machine learning algorithms, Journal of Cleaner Production, Vol. 350, 131364, Elsevier, 2022.
  9. Mehdi Yousefi, Reza Khandestani, Nima Gharaei-Moghaddam, Flexural behavior of reinforced concrete beams made of normal and polypropylene fiber-reinforced concrete containing date palm leaf ash, Structures, Vol. 37, pp. 1053-1068, Elsevier, 2022.
  10. Abdalla A., Salih Mohammed A. Surrogate Models to Predict the Long-Term Compressive Strength of Cement-Based Mortar Modified with Fly Ash, Archives of Computational Methods in Engineering, Vol. 29, pp. 4187-4212, Springer, 2022.
  11. Aso Abdalla, Ahmed Mohammed, Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network, and M5P-Tree Models to Predict The Long-Term of Compressive Strength of Cement- Mortar Modified With Fly Ash, Technical report, 2022.
  12. Abdalla A.A., Salih Mohammed A., Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD), Archives of Civil and Mechanical Engineering, Vol. 22, Nr. 105, Springer, 2022.
  13. Rafael Rivera-Lopez, Juana Canul-Reich, Efrén Mezura-Montes, Marco Antonio Cruz-Chávez, Induction of decision trees as classification models through metaheuristics, Swarm and Evolutionary Computation, Vol. 69, 101006, Elsevier, 2022.
  14. Kevin Dreßler, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo, ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression Learning, ACM Conference on Hypertext and Social Media, pp. 43-51, ACM Press, 2022.
  15. Amin Muhammad N., Kaffayatullah Khan, Muhammad F. Javed, Dina Y.Z. Ewais, Muhammad G. Qadir, Muhammad I. Faraz, Mir W. Alam, Anas A. Alabdullah, Muhammad Imran, Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming, Materials, Vol. 15, No. 11: 3808, MDPI, 2022.
  16. Pang Yingbo, Iftikhar Azim, Momina Rauf, Muhammad F. Iqbal, Xinguang Ge, Muhammad Ashraf, Muhammad A.U.R. Tariq, Anne W.M. Ng, Prediction of Bidirectional Shear Strength of Rectangular RC Columns Subjected to Multidirectional Earthquake Actions for Collapse Prevention, Sustainability, Vol. 14, No. 11: 6801, MDPI, 2022.
  17. Aso A. Abdalla, Ahmed Salih Mohammed, Serwan Rafiq, Riyadh Noaman, Warzer Sarwar Qadir, Kawan Ghafor, Hind AL-Darkazali, Raed Fairs, Microstructure, chemical compositions, and soft computing models to evaluate the influence of silicon dioxide and calcium oxide on the compressive strength of cement mortar modified with cement kiln dust, Construction and Building Materials, Vol. 341, 127668, Elsevier, 2022.
  18. Khan Kaffayatullah, Mohammed Ashfaq, Mudassir Iqbal, Mohsin A. Khan, Muhammad N. Amin, Faisal I. Shalabi, Muhammad I. Faraz, Fazal E. Jalal, Multi Expression Programming Model for Strength Prediction of Fly-Ash-Treated Alkali-Contaminated Soils, Materials, Vol. 15, No. 11: 4025, MDPI, 2022.
  19. Markus Jesswein, Jinyuan Liu, Using a genetic algorithm to develop a pile design method, Soils and Foundations, Vol. 62, Issue 4, 101175, Elsevier, 2022.
  20. Aso Abdalla, Ahmed Salih, Microstructure and chemical characterizations with soft computing models to evaluate the influence of calcium oxide and silicon dioxide in the fly ash and cement kiln dust on the compressive strength of cement mortar, Resources, Conservation & Recycling Advances, Vol. 15, 200090, Elsevier, 2022.
  21. Verma G., Kumar B., Application of multi-expression programming (MEP) in predicting the soaked California bearing ratio (CBR) value of fine-grained soil, Innovative Infrastructure Solutions, Vol. 7, 264, Springer, 2022.
  22. Zahid Awais, Ahmed Sarfraz, Irfan Muhammad, Experimental Investigation of Nano Materials Applicability in Hot Mix Asphalt (Hma), Construction and Building Materials, Vol. 350, 128882, Elsevier, 2022.
  23. Nassar M.A., PROTECTION AGAINST THE SCOUR AT OUTLETS OF CYLINDRICAL CULVERTS AND APPLICATION USING MULTI EXPRESSION PROGRAMMING TECHNIQUE (MEP), Engineering Heritage Journal (GWK), Vol. 6, Issue 1, pp. 14-18, 2022.
  24. Ruiliang Zhang, Xinhua Xue, Determining ultimate bearing capacity of shallow foundations by using multi expression programming (MEP), Engineering Applications of Artificial Intelligence, Vol. 115, 105255, Elsevier, 2022.
  25. Abdalla A., Mohammed A.S., Hybrid MARS-, MEP-, and ANN-based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content, Archives of Civil and Mechanical Engineering, 22, 194, Springer, 2022.
  26. Simó Muñoz Irene, Discovering new scaling laws in turbulent boundary layers via multi-expression programming, Bachelor thesis, Universitat Politecnica de Catalunya, 2022.
  27. Dioşan L., Andreica A., Voiculescu I., On the use of multi–objective evolutionary classifiers for breast cancer detection, PLoS ONE, Vol. 17(7), e0269950, 2022.
  28. Lee P. Leon, Hazi Azamathulla, Portia Felix, C. Venkata Siva Rama Prasad, Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming, Road Materials and Pavement Design, Taylor & Francis, 2022.
  29. Zia ur Rehman, Usama Khalid, Nauman Ijaz, Hassan Mujtaba, Abbas Haider, Khalid Farooq, Zain Ijaz, Machine learning-based intelligent modeling of hydraulic conductivity of sandy soil considering a wide range of grain sizes, Engineering Geology, Vol. 311, 106899, Elsevier, 2022.
  30. Gul, Muhammad Aniq, Md Kamrul Islam, Hamad Hassan Awan, Muhammad Sohail, Abdulrahman Fahad Al Fuhaid, Md Arifuzzaman, and Hisham Jahangir Qureshi, Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms, Symmetry, Vol. 14, No. 11: 2324, MDPI, 2022.
  31. Sanda-Maria Avram, Mihai Oltean, A comparison of several AI techniques for authorship attribution on Romanian texts, Mathematics, Vol. 10, Issue 23, 4589, MDPI, 2022.
  32. Qing-feng Liu, Progress and Research Challenges in Concrete Durability: Ionic Transport, Electrochemical Rehabilitation and Service Life Prediction, RILEM Technical Letters, Vol. 7, pp. 98-111, 2022.
  33. Alkadhim Hassan Ali, Amin Muhammad Nasir, Ahmad Izaz, Iqbal Mudassir, Khan Kaffayatullah, Al-Hashem Mohammed Najeeb, Khan Hayat, Jalal Fazal E., Prediction of Rapid Chloride Penetration Resistance of Metakaolin Based Concrete Using Multi-Expression Programming, Science of Advanced Materials, Vol. 14, No. 8, pp. 1348-1360, Ingenta Connect, 2022.
  34. Nassar M.A., Innovative Protection Against The Scour At Outlets Of Cylindrical Culverts And Application Using Multi Expression Programming Technique (Mep), Engineering Heritage Journal (GWK), Vol. 6, Issue 1, pp. 06-13, Zibeline International Publishing, 2022.

2023

  1. Mehdi Yousefi, Vajihe Khalili, Nima Gharaei-Moghaddam, Jorge de Brito, Evaluating the Mechanical Properties of Fibre-Reinforced Concrete Containing Coconut Palm Leaf Ash as Supplementary Cementitious Material, Iranian Journal of Science and Technology, Transactions of Civil Engineering, Vol. 47, pp. 909-924, Springer, 2023.
  2. Usama Mr. Muhammad, Gardezi Hasnain, Jalal Mr. Fazal E., Rehman Mr. Muhammad Ali, Javed Ms. Nida, Janjua Dr. Shahmir, Iqbal Mr. Mudassir, Predictive Modelling of Compression Strength of Waste Gp/Fa Blended Expansive Soils Using Multi-Expression Programming, Construction and Building Materials, Vol. 392, 131956, Elsevier, 2023.
  3. Cen Gao, Pu-Huai Lu, Wei-Min Ye, Zhang-Rong Liu, Qiong Wang, Yong-Gui Chen, Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability, Environmental Science and Pollution Research, Vol. 30, pp. 35872-35890, Springer, 2023.
  4. Ay M., Baykasoglu A., Ozbakir L., Kulluk S., A Case Study with the BEE-Miner Algorithm: Defects on the Production Line, In: Pham, D.T., Hartono, N. (eds), Intelligent Production and Manufacturing Optimisation—The Bees Algorithm Approach, Springer Series in Advanced Manufacturing, pp. 63-77, Springer, 2023.
  5. Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, Seyed Hosein Ghasemzadeh Mousavinejad, James Bristow, Vartenie Aramali, Moses Karakouzian, Predictive models for concrete properties using machine learning and deep learning approaches: A review, Journal of Building Engineering, Vol. 63, Part A, 105444, Elsevier, 2023.
  6. Fadi Althoey, Muhammad Naveed Akhter, Zohaib Sattar Nagra, Hamad Hassan Awan, Fayez Alanazi, Mohsin Ali Khan, Muhammad Faisal Javed, Sayed M Eldin, Yasin Onuralp Özkılıç, Prediction Models for Marshall Mix Parameters Using Bio-inspired Genetic Programming and Deep Machine Learning Approaches: A Comparative Study, Case Studies in Construction Materials, Vol. 18, e01774, Elsevier, 2023.
  7. Fazal E. Jalal, Mudassir Iqbal, Mohsin Ali Khan, Babatunde A. Salami, Shahid Ullah, Hayat Khan, Marwa Nabil, Indirect Estimation of Swelling Pressure of Expansive Soil: GEP versus MEP Modelling, Advances in Materials Science and Engineering, Vol. 2023, Article ID 1827117, Hindawi, 2023.
  8. Kuranga C., Pillay N., A Comparative Study of Genetic Programming Variants, In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds), Artificial Intelligence and Soft Computing, ICAISC 2022, LNCS, Vol. 13588, pp. 377-386, Springer, 2023.
  9. Ali Aldrees, Muhammad Faisal Javed, Abubakr Taha Bakheit Taha, Abdeliazim Mustafa Mohamed, Michał Jasiński, Miroslava Gono, Evolutionary and ensemble machine learning predictive models for evaluation of water quality, Journal of Hydrology: Regional Studies, Vol. 46, 101331, Elsevier, 2023.
  10. Kashif Nazir, Shazim Ali Memon, Assemgul Saurbayeva, Abrar Ahmad, Energy consumption predictions by genetic programming methods for PCM integrated building in the tropical savanna climate zone, Journal of Building Engineering, Vol. 68, 106115, Elsevier, 2023.
  11. Hemn Unis Ahmed, Ahmed S. Mohammed, Rabar H. Faraj, Aso A. Abdalla, Shaker M. A. Qaidi, Nadhim Hamah Sor, Azad A. Mohammed, Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles, Neural Computing and Applications, Vol. 35, pp. 12453-12479, Springer, 2023.
  12. Fadi Althoey, Nadhim Hamah Sor, Haitham M. Hadidi, Syed Farasat Ali Shah, Abdulaziz Alaskar, Sayed M Eldin, Tariq Bashir, Muhammad Faisal Javed, Crack Width Prediction of Self-Healing Engineered Cementitious Composite Using multi-expression programming, Journal of Materials Research and Technology, Vol. 24, pp. 918-927, Elsevier, 2023.
  13. Nima Gharaei-Moghaddam, Alireza Arabshahi, Mohammadreza Tavakkolizadeh, Predictive models for the peak stress and ultimate strain of FRP confined concrete cylinders with inclined fiber orientations, Results in Engineering, Vol. 18, 101044, Elsevier, 2023.
  14. Lingling Chen, Zhiyuan Wang, Aftab Ahmad Khan, Majid Khan, Muhammad Faisal Javed, Abdulaziz Alaskar, Sayed M. Eldin, Development of predictive models for sustainable concrete via genetic programming-based algorithms, Journal of Materials Research and Technology, Vol. 24, pp. 6391-6410, Elsevier, 2023.
  15. Muhammad Nasir Amin, Waqas Ahmad, Kaffayatullah Khan, Ahmed Farouk Deifalla, Optimizing compressive strength prediction models for rice husk ash concrete with evolutionary machine intelligence techniques, Case Studies in Construction Materials, Vol. 18, e02102, Elsevier, 2023.
  16. Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin, Afnan Nafees, Fadi Althoey, Akbar Niaz, A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin, Structures, Vol. 53, pp. 937-948, Elsevier, 2023.
  17. Peshkawt Yaseen Saleh, Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, Hemn Unis Ahmed, Rabar H. Faraj, Wael Mahmood, Ahmed Salih Mohammed, Prediction of the compressive strength of strain-hardening cement-based composites using soft computing models, Structural Concrete, John Wiley & Sons, 2023.
  18. Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey, Mohammed Awad Abuhussain, Muhammad Faisal Javed, Ahmed Farouk Deifalla, Nivin A. Ghamry, Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature, Case Studies in Construction Materials, Vol. 18, e02199, Elsevier, 2023.
  19. Saeed Talamkhani, Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis, Advances in Civil Engineering, Vol. 2023, Article ID 3692090, Hindawi, 2023.
  20. Xiaojie Yuan, Xinhua Xue, Prediction of soil thermal conductivity using artificial intelligence approaches, Geothermics, Vol. 113, 102769, Elsevier, 2023.
  21. Xinliang Zheng, Yi Xie, Xujiao Yang, Muhammad Nasir Amin, Sohaib Nazar, Suleman Ayub Khan, Fadi Althoey, Ahmed Farouk Deifalla, A data-driven approach to predict the compressive strength of alkali-activated materials and correlation of influencing parameters using SHapley Additive exPlanations (SHAP) analysis, Journal of Materials Research and Technology, Vol. 25, pp. 4074-4093, Elsevier, 2023.
  22. Hemn Unis Ahmed, Ahmed S. Mohammed, Azad A. Mohammed, Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with Nano-silica: Experimental and computational investigation, Construction and Building Materials, Vol. 394, 132266, Elsevier, 2023.
  23. Tianyu Wang, Philippe Reiffsteck, Christophe Chevalier, Chi-Wei Chen, Franziska Schmidt, An interpretable model for bridge scour risk assessment using explainable artificial intelligence and engineers' expertise, Structure and Infrastructure Engineering, Taylor & Francis, 2023.
  24. Sultan Shah, Moustafa Houda, Sangeen Khan, Fadi Althoey, Maher Abuhussain, Muhammed Awad Abuhussain, Mujahid Ali, Abdulaziz Alaskar, Muhammad Faisal Javed, Mechanical behavior of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP), Journal of Materials Research and Technology, Vol. 25, pp. 5720-5740, Elsevier, 2023.
  25. Mansour Ghalehnovi, Mohammad Meghdadian, Proposition of Design Relations for Composite Steel Plate Shear Walls Containing an Opening, International Journal of Advanced Scientific Research and Innovation, Vol. 6(1), pp. 1-18. 2023.
  26. Fazal E. Jalal, Mudassir Iqbal, Unconfined compression strength modelling of expansive soils for sustainable construction: GEP vs MEP, Environmental Earth Sciences, Vol. 82, 364, Springer, 2023.
  27. Bawar Iftikhar, Sophia C. Alih, Mohammadreza Vafaei, Muhammad Faisal Javed, Muhammad Faisal Rehman, Sherzod Shukhratovich Abdullaev, Nissren Tamam, M. Ijaz Khan, Ahmed M. Hassan, Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming, Scientific Reports, Vol. 13, 12149, Springer Nature, 2023.
  28. Adnan Khan, Ju Huyan, Runhua Zhang, Yu Zhu, Weiguang Zhang, Gao Ying, Kamal Nasir Ahmad, Syed Khaliq Shah, An ensemble tree-based prediction of Marshall mix design parameters and resilient modulus in stabilized base materials, Construction and Building Materials, Vol. 401, 132833, Elsevier, 2023.
  29. Arnau Miró, Stefan Wallin, Alessandro Colombo, Lionel Temmerman, Dirk Wunsch, Oriol Lehmkuhl, Towards a machine learning model for Explicit Algebraic Reynolds Stress Modelling using Multi-Expression Programming, ERCOFTAC Symposium on Engineering Turbulence Modelling and Measurement (ETMM14), 2023.
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