The Gene Expression Programming Bibliography

As the number of papers on GEP grows, it’s proving to be very hard for me to keep the GEP Bibliography up to date. I have plans to automate the entries and make the biblio searchable, but till then it would be a great help if you could send me the complete reference of your work together with the link to the paper or DOI. The link is not mandatory but it is essential if you want your work to be read by a wider audience. Many thanks! 

Books:

	1. Ferreira, C., 2006. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2nd Edition, Springer-Verlag, Germany. More...
	2. Ferreira, C., 2002. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Angra do Heroismo, Portugal. Online version

Papers:

2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008

Papers 2001

1. Ferreira, C., 2001. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, Vol. 13, issue 2: 87-129. paper

(You can also download the pdf of the first public version of this paper, published on the internet November 14, 2000: the English version and the Portuguese translation.)

2. Ferreira, C., 2001. Gene Expression Programming in Problem Solving, invited tutorial of the 6th Online World Conference on Soft Computing in Industrial Applications, September 10-24, 2001. paper

Papers 2002

1. Ferreira, C., Mutation, Transposition, and Recombination: An Analysis of the Evolutionary Dynamics. In H. J. Caulfield, S.-H. Chen, H.-D. Cheng, R. Duro, V. Honavar, E. E. Kerre, M. Lu, M. G. Romay, T. K. Shih, D. Ventura, P. P. Wang, Y. Yang, eds., Proceedings of the 6th Joint Conference on Information Sciences, 4th International Workshop on Frontiers in Evolutionary Algorithms, pages 614-617, Research Triangle Park, North Carolina, USA, 2002. paper

2. Ferreira, C., Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming. In E. Lutton, J. A. Foster, J. Miller, C. Ryan, and A. G. B. Tettamanzi, eds., Proceedings of the 4th European Conference on Genetic Programming, EuroGP 2002, Vol. 2278 of Lecture Notes in Computer Science, pages 51-60, Springer-Verlag, Berlin, Germany, 2002. paper

3. Ferreira, C., Gene Expression Programming in Problem Solving. In R. Roy, M. Köppen, S. Ovaska, T. Furuhashi, and F. Hoffmann, eds., Soft Computing and Industry: Recent Applications, pages 635-654, Springer-Verlag, 2002 (shorter version of the invited tutorial presented at the 6th Online World Conference on Soft Computing in Industrial Applications, September 10-24, 2001). paper

4. Ferreira, C., Combinatorial Optimization by Gene Expression Programming: Inversion Revisited. In J. M. Santos and A. Zapico, eds., Proceedings of the Argentine Symposium on Artificial Intelligence, pages 160-174, Santa Fe, Argentina, 2002. paper

5. Ferreira, C., Function Finding and the Creation of Numerical Constants in Gene Expression Programming. Seventh Online World Conference on Soft Computing in Industrial Applications, September 23 - October 4, 2002. paper

6. Ferreira, C., Analyzing the Founder Effect in Simulated Evolutionary Processes Using Gene Expression Programming. In A. Abraham, J. Ruiz-del-Solar, and M. Köppen, eds., Soft Computing Systems: Design, Management and Applications, pp. 153-162, IOS Press, Netherlands, 2002. paper

7. Ferreira, C., 2002. Genetic Representation and Genetic Neutrality in Gene Expression Programming. Advances in Complex Systems, 5 (4): 389-408. paper

8. Chi Zhou, Peter C. Nelson, Weimin Xiao, and Thomas M. Tirpak, Discovery of Classification Rules by Using Gene Expression Programming. In Proceedings of the International Conference on Artificial Intelligence, pages 1355-1361, Las Vegas, USA, 2002.

9. Yorick Hardy and W.-H. Steeb, 2002. Gene Expression Programming and One-dimensional Chaotic Maps. International Journal of Modern Physics C, 13 (1): 25-30. paper

10. Jie Zuo, Changjie Tang, and Tianqing Zhang, Mining Predicate Association Rule by Gene Expression Programming. In X. Meng, J. Su, and Y. Wang, eds, Proceedings of the Third International Conference on Advances in Web-Age Information Management, Lecture Notes In Computer Science, Vol. 2419, pp. 92-103, Beijing, China, 2002. paper

Papers 2003

1. Ferreira, C., Function Finding and the Creation of Numerical Constants in Gene Expression Programming. In J. M. Benitez, O. Cordon, F. Hoffmann, and R. Roy, eds., Advances in Soft Computing: Engineering Design and Manufacturing, pages 257-266, Springer-Verlag, 2003 (shorter version of the paper presented at the 7th Online World Conference on Soft Computing in Industrial Applications, 2002). paper

2. Chi Zhou, Weimin Xiao, Peter C. Nelson, and Thomas M. Tirpak, 2003. Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, pages 519-531. paper

3. Serdal Terzi and Mehmet Saltan, Modeling the Deflection Basin by Genetic Algorithms Approach. In Proceedings of the 12th International Turkish Symposium on Artificial Intelligence and Neural Networks – TAINN 2003, Çanakkale, Turkey, 2003. paper

4. Hongqing Cao, Jingxian Yu, and Lishan Kang, An Evolutionary Approach for Modeling the Equivalent Circuit for Electrochemical Impedance Spectroscopy. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC2003), Vol. 3, pp. 1819-1825, IEEE Press, Canberra, Australia, 2003. paper

Papers 2004

1. Ferreira, C., Gene Expression Programming and the Evolution of Computer Programs. In Leandro N. de Castro and Fernando J. Von Zuben, eds., Recent Developments in Biologically Inspired Computing, pages 82-103, Idea Group Publishing, 2004. paper

2. Ferreira, C., Designing Neural Networks Using Gene Expression Programming. Nineth Online World Conference on Soft Computing in Industrial Applications, September 20 - October 8, 2004. paper

3. Jie Zuo, Chang-jie Tang, Chuan Li, Chang-an Yuan and An-long Chen, Time Series Prediction Based on Gene Expression Programming. In Advances in Web-Age Information Management, Vol. 3129 of Lecture Notes in Computer Science, pages 55-64, Springer, Germany, 2004. paper

4. Xin Li, Chi Zhou, Peter C. Nelson, and Thomas M. Tirpak, Investigation of Constant Creation Techniques in the Context of Gene Expression Programming. In M. Keijzer, ed., Late Breaking Paper at Genetic and Evolutionary Computation Conference, GECCO-2004, Seattle, Washington, USA, June 26-30, 2004. paper

5. Edwin Roger Banks, James C. Hayes, and Edwin Núñez, Parametric Regression Through Genetic Programming. In M. Keijzer, ed., Late Breaking Paper at Genetic and Evolutionary Computation Conference, GECCO-2004, Seattle, Washington, USA, June 26-30, 2004. paper

6. E. R. Banks, J. C. Hayes, and E. Núñez. Parametric Regression Through Genetic Programming. In R. Poli, S. Cagnoni, M. Keijzer, E. Costa, F. Pereira, G. Raidl, S.C. Upton, D. Goldberg, H. Lipson, E. de Jong, J. Koza, H. Suzuki, H. Sawai, I. Parmee, M. Pelikan, K. Sastry, D. Thierens, W. Stolzmann, P.L. Lanzi, S.W. Wilson, M. O'Neill, C. Ryan, T. Yu, J.F. Miller, I. Garibay, G. Holifield, A.S. Wu, T. Riopka, M.M. Meysenburg, A.W. Wright, N. Richter, J.H. Moore, M.D. Ritchie, L. Davis, R. Roy, and M. Jakiela, eds., GECCO 2004 Workshop Proceedings, 2004. paper

7. Shu-Heng Chen and Bin-Tzong Chie, Toward a Functional Modularity Approach to Agent-Based Modeling of the Evolution of Technology. In Proceedings of the 9th Workshop on Economics and Heterogeneous Interacting Agents, Kyoto, Japan, 2004. paper

8. Zhuli Xie, Xin Li, Barbara Di Eugenio, Weimin Xiao, Thomas M. Tirpak, and Peter C. Nelson, Using Gene Expression Programming to Construct Sentence Ranking Functions for Text Summarization. In Proceedings of the 20th International Conference on Computational Linguistics, Geneva, Switzerland, August 23-27, 2004. paper

9. Heitor S. Lopes and Wagner R. Weinert, 2004. EGIPSYS: An Enhanced Gene Expression Programming Approach for Symbolic Regression Problems. International Journal of Applied Mathematics and Computer Science, 14 (3): 375-384. paper

10. K.J. Brazier, G. Richards and W. Wang, Implicit fitness sharing speciation and emergent diversity in tree classifier ensembles. In Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, Exeter, UK, August 25-27, 2004. paper

11. Baykasoglu, A., Dereli, T., Tanış, S., 2004. Prediction of cement strength using soft computing techniques. Cement and Concrete Research, 34: 2083-2090. paper

12. L. Zielinski and J. Rutkowski, Design Tolerancing with Utilization of Gene Expression Programming and Genetic Algorithm. In Proceedings of the International Conference on Signals and Electronic Systems, ICSES 2004, pp. 381-384, Poznan, Poland, 2004. paper

13. Lopes, H.S. and W.R. Weinert, A gene expression programming system for time series modeling. In Proceedings of XXV Iberian Latin American Congress on Computational Methods in Engineering ,CILAMCE 2004, Recife, Brazil, 10-12 November, 2004. paper

14. Li Qu, Cai Zhihua, Jiang Siwei, Zhu Li, Gene expression programming in prediction. In Proceedings of the Fifth World Congress on Intelligent Control and Automation (WCICA), Vol. 3, pp. 2171-2175, 2004. paper

15. Li Qu, Cai Zhihua, Zhu Li, Zhao Yunsheng, 2004. Application of gene expression programming in predicting the amount of gas emitted from coal face. Journal of Basic Science and Engineering, Vol. 12, No. 1: 49-54.

16. Cai Zhihua, Li Qu, Jiang Siwei, Zhu Li, Symbolic regression based on GEP and its application in predicting amount of gas emitted from coal face, In Proceedings of the 2004 International Symposium on Safety Science and Technology, pp. 637-641, 2004.

Papers 2005

1. Mehmet Saltan & Serdal Terzi, 2005. Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness. Indian Journal of Engineering & Materials Sciences, Vol. 12, No 1, pp. 42-50. paper

2. Serdal Terzi, 2005. Modeling the Deflection Basin of Flexible Highway Pavements by Gene Expression Programming. Journal of Applied Sciences 5 (2): 309-314. paper

3. Juan J. Flores, Mario Graff, and Cadenas Erasmo. Wind Prediction using Genetic Algorithms and Gene Expression Programming. In Proceedings of the International Conference on Modelling and Simulation in the Enterprises, AMSE 2005, Morelia, Mexico, 2005. paper

4. Özlem Terzi and M. Erol Keskin, 2005. Evaporation Estimation Using Gene Expression Programming. Journal of Applied Sciences 5(3): 508-512. paper

5. Xin Li, Chi Zhou, Weimin Xiao, and Peter C. Nelson, Prefix Gene Expression Programming. In Late Breaking Paper at Genetic and Evolutionary Computation Conference, GECCO-2005, Washington, D.C., USA, June 25-29, 2005. paper

6. D. González Muñoz, O. Gustafsson, and L. Wanhammar, Evolution of filter order equations for linear-phase FIR filters using gene expression programming. In Proceedings of the National Conference on Radio Science and Communication, RVK 2005, pp. 679-682, Linköping, Sweden, June 14-16, 2005. paper

7. Baykasoglu, A., Soft computing approaches to production line design. In Proceedings of the 3rd International Conference on Responsive Manufacturing, ICRM 2005, pp. 273-279, Guangzhou, China, 2005. paper

8. M. H. Marghny and I. E. El-Semman, Extracting Logical Classification Rules with Gene Expression Programming: Microarray Case Study. In Proceedings of the International Conference on Artificial Intelligence and Machine Learning, AIML 2005, Cairo, Egypt, 2005. paper

9. M. H. Marghny and I. E. El-Semman, Extracting Fuzzy Classification Rules with Gene Expression Programming. In Proceedings of the International Conference on Artificial Intelligence and Machine Learning, AIML 2005, Cairo, Egypt, 2005. paper

10. Xin Li, Chi Zhou, Weimin Xiao, and Peter C. Nelson, Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures. In Proceedings of the 4th International Conference on Machine Learning and Applications, ICMLA 2005, pp. 337-342, Los Angeles, CA, USA, 2005. paper

11. Robert J. Pelton, David Japikse, Daniel Maynes, and Kerry N. Oliphant, Turbomachinery Performance Models. In Proceedings of the International Mechanical Engineering Congress & Exposition, IMECE 2005, Orlando, Florida, USA, 2005. paper

12. Teodorescu, L., High energy physics data analysis with gene expression programming. In 2005 IEEE Nuclear Science Symposium Conference Record, Vol. 1, pp. 143-147, 2005. paper

13. Chuan-Sheng Wu, Li Huang, and Li-Shan Kang, The automatic modeling of complex functions based on gene expression programming. In Proceedings of 2005 Machine Learning and Cybernetics, Volume 5, pp. 2870-2873, 2005. paper

14. Antoine B. Bagula and Hong F. Wang, On the Relevance of Using Gene Expression Programming in Destination-Based Traffic Engineering. In Computational Intelligence and Security, Vol. 3801 of Lecture Notes in Computer Science, pages 224-229, Springer, Germany, 2005. paper

15. Elena Bautu, Andrei Bautu, and Henri Luchian, Symbolic Regression on Noisy Data with Genetic and Gene Expression Programming. In Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2005, pp. 321-324, 2005. paper

16. David Japikse, Oleg Dubitsky, Kerry N. Oliphant, Robert J. Pelton, Daniel Maynes, and Jamin Bitter, Multi-Variable, High Order, Performance Models. In Proceedings of the International Mechanical Engineering Congress & Exposition, ASME 2005, Orlando, Florida, USA, 2005. paper

17. Xin Li, Self-Emergence of Structures in Gene Expression Programming. In Proceedings of the AAAI/SIGART Doctoral Consortium 2005, Pittsburgh, Pennsylvania, USA, 2005. paper

18. Edwin Roger Banks, Edwin Núñez, Paul Agarwal, Claudette Owens, Marshall McBride, and Ron Liedel, Genetic Programming for Discrimination of Buried Unexploded Ordnance (UXO). In Franz Rothlauf, ed., Late Breaking Paper at Genetic and Evolutionary Computation Conference, GECCO 2005, Washington, D.C., USA, 2005. paper

19. Kangshun Li, Yuanxiang Li, Haifang Mo, and Zhangxin Chen, 2005. A new algorithm of evolving artificial neural networks via gene expression programming. Journal of the Korea Society for Industrial and Applied Mathematics, Vol. 9, No. 2: 83-90.

20. Keskin, M.E. and Ö. Terzi, Modeling Water Temperature Using Gene Expression Programming. In Proceedings of the 14th Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN 2005, pages 280-285, Izmir, Turkey, 2005.

21. W. Zhu and H. Timmermans, Exploring pedestrian shopping decision processes: An application of gene expression programming. In Proceedings of the Third International Conference on Pedestrian and Evacuation Dynamics, Vienna, Austria, 2005.

22. Juan J. Flores and Mario Graff. System Identification Using Genetic Programming and Gene Expression Programming. In Pinar Yolum, Tunga Gungor, Fikret Gurgen, and Can Ozturan, eds., Proceedings of the 20th International Symposium Computer and Information Sciences, ISCIS 2005, Vol. 3733 of Lecture Notes in Computer Science, pages 503-511, Springer, Germany, 2005. paper

23. Jiang Siwei, Cai Zhihua, Zeng Dan, Liu Yadong, and Li Qu, Gene expression programming based on simulated annealing. In Proceedings of the 2005 International Conference on Wireless Communications, Networking and Mobile Computing, pages 1264-1267, Wuhan, China, 2005. paper

24. Litvinenko, V.I., P.I. Bidyuk, J.N. Bardachov, V.G. Sherstjuk, and A.A. Fefelov, Combining Clonal Selection Algorithm and Gene Expression Programming for Time Series Prediction. In Proceedings of the Third Workshop 2005 IEEE Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2005, pages 133-138, Sofia, Bulgaria, 2005. paper

25. David González Muñoz, 2005. Discovering unknown equations that describe large data sets using genetic programming techniques. Master Thesis in Electronic Systems, Linköping Institute of Technology, Sweden. thesis

26. Elena Bautu, Andrei Bautu, and Henri Luchian, A GEP-Based Approach for Solving Fredholm First Kind Integral Equations. In Proceedings of the Seventh international Symposium on Symbolic and Numeric Algorithms For Scientific Computing, SYNASC 2005, IEEE Computer Society, Washington, DC, pp. 325-329, 2005. paper

Papers 2006

1. Ferreira, C., Automatically Defined Functions in Gene Expression Programming. In N. Nedjah, L. de M. Mourelle, A. Abraham, eds., Genetic Systems Programming: Theory and Experiences, Studies in Computational Intelligence, Vol. 13, pp. 21-56, Springer-Verlag, 2006. paper

2. Ferreira, C., Designing Neural Networks Using Gene Expression Programming. In A. Abraham, B. de Baets, M. Köppen, and B. Nickolay, eds., Applied Soft Computing Technologies: The Challenge of Complexity, pages 517-536, Springer-Verlag, 2006 (this is the revised version of the paper presented at the 9th Online World Conference on Soft Computing in Industrial Applications, September 20 - October 8, 2004). paper

3. Weinert, W.R. and H.S. Lopes, GEPCLASS: A classification rule discovery tool using gene expression programming. In Proceedings of the Second International Conference on Advanced Data Mining and Applications, ADMA 2006, Lecture Notes in Computer Science, Vol. 4093, pp. 871-880, Xi’an, China, August 14-16, 2006. paper

4. Teodorescu, L., 2006. Gene Expression Programming Approach to Event Selection in High Energy Physics. IEEE Transactions on Nuclear Science, Vol. 53, Issue 4: 2221-2227. paper

5. C. Yuan, C. Tang, Y. Wen, J. Zuo, J. Peng, and J. Hu, 2006. Convergency of Genetic Regression in Data Mining Based Gene Expression Programming and Optimized Solution. International Journal of Computers and Applications, Vol. 202, Issue 4: 1831-1834. paper

6. Bagula, A.B., Traffic Engineering Next Generation IP Networks Using Gene Expression Programming. In Proceedings of the 10th IEEE/IFIP Network Operations and Management Symposium, NOMS 2006, pp. 230-239, Vancouver, Canada, 2006. paper

7. Bagula, A.B., 2006. Hybrid Routing in Next Generation IP Networks: QoS Routing Mechanisms and Network Control Strategies. Doctoral Thesis, Electronic, Computer, and Software Systems, Stockholm, Sweden. thesis

8. Weihong Wang, Qu Li, Shanshan Han, and Hai Lin, A Preliminary Study on Constructing Decision Tree with Gene Expression Programming. In Proceedings of the First International Conference on Innovative Computing, Information and Control, ICICIC 2006, Volume I, pp. 222-225, 2006. paper

9. Xue-song Yan, Wei Wei, Rui Liu, San-you Zeng, and Li-shan Kang, Designing Electronic Circuits by Means of Gene Expression Programming. In Proceedings of the First NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2006, pp. 194-199, 2006. paper

10. Shaojie Qiao, Changjie Tang, Jing Peng, Hongjian Fan, and Yong Xiang, VCCM Mining: Mining Virtual Community Core Members Based on Gene Expression Programming. In Intelligence and Security Informatics, Vol. 3917 of Lecture Notes in Computer Science, pages 133-138, Springer, Germany, 2006. paper

11. Changjie Tang, Lei Duan, Jing Peng, Huan Zhang, and Yixiao Zong, The Strategies to Improve Performance of Function Mining By Gene Expression Programming: Genetic Modifying, Overlapped Gene, Backtracking and Adaptive Mutation. In Proceedings of the 17th Data Engineering Workshop, DEWS 2006, Ginowan, Japan, 2006. paper

12. Samuel Ashworth, Gene Expression Programming Applied to Image Compression. Technical Report, NSF Research Experiences for Undergraduates, Computer Vision and Image Processing, Department of Computer Science, Utah State University, 2006. paper

13. Robert Gempeler, Image Compression Using Gene Expression Programming. Technical Report, NSF Research Experiences for Undergraduates, Computer Vision and Image Processing, Department of Computer Science, Utah State University, 2006. paper

14. Lei Duan, Changjie Tang, Tianqing Zhang, Dagang Wei, and Huan Zhang, Distance Guided Classification with Gene Expression Programming. In Advanced Data Mining and Applications, Vol. 4093 of Lecture Notes in Computer Science, pages 239-246, Springer, Germany, 2006. paper

15. Tao Zeng, Changjie Tang, Yintian Liu, Jiangtao Qiu, Mingfang Zhu, Shucheng Dai, and Yong Xiang, Mining h-Dimensional Enhanced Semantic Association Rule Based on Immune-Based Gene Expression Programming. In Web Information Systems – WISE 2006 Workshops, Vol. 4256 of Lecture Notes in Computer Science, pages 49-60, Springer, Germany, 2006. paper

16. Yanchao Liu, Liang Gao, Yan Dong, and Baolin Pan, A New Method for Finding Constant Terms in the Context of Gene Expression Programming. In Proceedings of the International Conference Bio-Inspired Computing – Theory and Applications, BIC-TA 2006, pp. 195-200, Wuhan, China, 2006. paper

17. Valdés, J. and A. Barton, Relevant Attribute Discovery in High Dimensional Data: Application to Breast Cancer Gene Expressions. In Proceedings of the First International Conference on Rough Sets and Knowledge Technology, RSKT 2006, Chongqing, China, 2006. paper

18. Ajith Abraham and Crina Grosan, 2006. Decision Support Systems Using Ensemble Genetic Programming. Journal of Information & Knowledge Management, Vol. 5, No. 4: 303–313. paper

19. Ajith Abraham and Crina Grosan, 2006. Automatic Programming Methodologies for Electronic Hardware Fault Monitoring. Journal of Universal Computer Science, Vol. 12, No. 4: 408-431. paper

20. M. Dayik, M., M.C. Kayacan, H. Çalis, and E. Çakmak, 2006. Control of warp tension during weaving procedure using evaluation programming. The Journal of the Textile Institute, Vol. 97, Issue 4: 313-324. paper

21. Vassilios K. Karakasis and Andreas Stafylopatis, Data Mining based on Gene Expression Programming and Clonal Selection. In Proceedings of the IEEE World Congress on Evolutionary Computation, CEC 2006, pages 514-521, 2006. paper

22. Zhuo Kang, Lishan Kang, and Yan Li, A New Automatic Programming Method for Program Reuse. In Proceedings of the 3rd International Conference on Neural, Parallel and Scientific Computations, Atlanta, USA, 2006.

23. Edwin Núñez, Paul Agarwal, Marshall McBride, Ron Liedel, and Claudette Owens, 2006. Modeling and Simulation Optimization Using Evolutionary Computation. Technical Report, Colsa Corporation, Huntsville, USA. paper

24. Wei Zhu and Harry Timmermans, Exploring Heuristics Underlying Pedestrian Shopping Decision Processes: An Application of Gene Expression Programming. In Jos P. Van Leeuwen and Harry J. P. Timmermans, eds., Innovations in Design & Decision Support Systems in Architecture and Urban Planning, pages 121-136, Springer, Netherlands, 2006. paper

25. H. Z. Si, K. J. Zhang, Z. D. Hu, B. T. Fan, 2006. QSAR Model for Prediction Capacity Factor of Molecular Imprinting Polymer Based on Gene Expression Programming. QSAR & Combinatorial Science, Vol. 26, Issue 1: 41-50. paper

26. Valdés, J. and A. Barton, Virtual Reality Visual Data Mining via Neural Networks Obtained from Multiobjective Evolutionary Optimization: Application to Geophysical Prospecting. In Proceedings of the 2006 IEEE International Joint Conference on Neural Networks, IJCNN 2006, Vancouver, Canada, 2006. paper

27. Kwasnicka H. and P. Wozniak, EMOT - Evolutionary Approach to 3D Computer Animation. In Proceedings of the International Multiconference on Computer Science and Information Technology, Wisla, Poland. 2006. paper

28. Hong Zong Si, Tao Wang, Ke Jun Zhang, Zhi De Hu, and Bo Tao Fan, 2006. QSAR study of 1,4-dihydropyridine calcium channel antagonists based on gene expression programming. Bioorganic & Medicinal Chemistry, Vol. 14, Issue 14: 4834-4841. paper

29. Kejun Zhang, Yuxia Hu and Gang Liu, An Improved Gene Expression Programming for Solving Inverse Problem. In Proceedings of the Sixth World Congress on Intelligent Control and Automation, WCICA 2006, pages 3371-3375, Dalian, China, 2006. paper

30. Stefan Kahrs, Gene expression programming with pre-order traversals. In Konstantinos Sirlantzis, ed., Proceedings of the 6th International Conference on Recent Advances in Soft Computing, pp. 84-89, 2006. slideshow

Papers 2007

1. W. Zhu and H. Timmermans, Exploring pedestrian shopping decision processes: An application of gene expression programming. In Nathalie Waldau, Peter Gattermann, Hermann Knoflacher, Michael Schreckenberg, eds., Pedestrian and Evacuation Dynamics 2005, pages 145-154, Springer-Verlag, Germany, 2007.

2. H.H. Park, A. Grings, M.V. dos Santos, and A. S. Soares, 2007. Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming. Applied Mathematics and Computation, in press. paper

3. Abdulkadir Cevik, 2007. A new formulation for web crippling strength of cold-formed steel sheeting using genetic programming. Journal of Constructional Steel Research, vol. 63, no. 7, pp. 867-883. paper

4. Elena Bautu, Andrei Bautu, and Henri Luchian, AdaGEP - An Adaptive Gene Expression Programming Algorithm. In Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, IEEE Computer Society, Washington, DC, USA, pp. 403-406, 2007. paper

5. Dazhi Jiang, Zhijian Wu, and Lishan Kang, Parameter Identifications In Differential Equations By Gene Expression Programming. In Proceedings of the Third International Conference on Natural Computation (ICNC 2007), pp. 644-648, 2007. paper

6. Hui-yun Quan and Guangyi Yang, Gene Expression Programming with DAG Chromosome. In Proceedings of the Second International Symposium on Advances in Computation and Intelligence, ISICA 2007, Lishan Kang, Yong Liu, and Sanyou Y. Zeng, eds., Wuhan, China, pp. 271-275, 2007. paper

7. Yanchao Liu , John English, and Edward Pohl, Application of Gene Expression Programming in the Reliability of Consecutive-k-out-of-n: F Systems with Identical Component Reliabilities. In De-Shuang Huang, Laurent Heutte, and Marco Loog, eds., Proceedings of the Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, Springer Berlin Heidelberg, pp. 217-224, 2007. paper

Papers 2008

1. Stewart W. Wilson, 2008. Classifier Conditions Using Gene Expression Programming. IlliGAL Report No. 2008001, University of Illinois at Urbana-Champaign, USA. paper

2. Khalid Eldrandaly and Abdelazim Negm, 2008. Performance Evaluation of Gene Expression Programming for Hydraulic Data Mining. International Arab Journal of Information Technology, vol. 5, no. 2, pp. 126-131.

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