Academic Citations
GPTIPS has been used as a technology platform for a variety of diverse research and teaching applications. See the following resources for details.
When Darwin meets Lorenz: evolving new chaotic attractors through genetic programming
Indranil Pan and Saptarshi Das
Chaos, Solitons & Fractals, Vol. 76, pp. 141-155, DOI: 10.1016/j.chaos.2015.03.017, Elsevier, July 2015.
Link: http://dx.doi.org/10.1016/j.chaos.2015.03.017
Lateral load capacity of piles in clay using genetic programming and multivariate adaptive regression spline
Pradyut Kumar Muduli, Manas Ranjan Das, Sarat Kumar Das & Swagatika Senapati
Indian Geotechnical Journal, DOI: 10.1007/s40098-014-0142-2, Springer India, Jan. 2015.
Link: http://dx.doi.org/10.1007/s40098-014-0142-2
Evolving functional expression of permeability of fly ash by a new evolutionary approach
Ankit Garg, Akhil Garg & Jasmine Siu Lee Lam
Transport in Porous Media, DOI: 10.1007/s11242-015-0454-4, Springer Netherlands, Jan. 2015.
Link: http://dx.doi.org/10.1007/s11242-015-0454-4
A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool
Hossam Farisa & Alaa Shetab
International Journal of Computer Integrated Manufacturing, DOI: 10.1080/0951192X.2014.1002809, Jan. 2015.
Link: http://dx.doi.org/10.1080/0951192X.2014.1002809
Machine learning utilization for bed load transport in gravel-bed rivers
Vasileios Kitsikoudis, Epaminondas Sidiropoulos & Vlassios Hrissanthou
Water Resources Management, Vol. 28, pp. 3727–3743, DOI: 10.1007/s11269-014-0706-z, Springer, 2014.
Link: http://dx.doi.org/10.1007/s11269-014-0706-z
Triple bottomline many‐objective‐based decision making for a land use management problem
Oliver Chikumbo, Erik Goodman & Kalyanmoy Deb
Journal of Multi‐Criteria Decision Analysis, DOI: 10.1002/mcda.1536, pp. 1099-1360, Wiley, Dec. 2014.
Link: http://dx.doi.org/10.1002/mcda.1536
Process characterisation of 3D-printed FDM components using improved evolutionary computational approach
Vijayaraghavan, V., Garg, A., Lam, J. S. L., Panda, B. & Mahapatra, S. S.
The International Journal of Advanced Manufacturing Technology, 1-13, DOI 10.1007/s00170-014-6679-5, 2014.
Link: http://dx.doi.org/10.1007/s00170-014-6679-5
An integrated computational approach for determining the elastic properties of boron nitride nanotubes
V. Vijayaraghavan, A. Garg, C. H. Wong, K. Tai & Pravin M. Singru
International Journal of Mechanics and Materials in Design, pp. 1569-1713, DOI: 10.1007/s10999-014-9262-1, Springer, June 2014.
Link: http://dx.doi.org/10.1007/s10999-014-9262-1
Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters
Alamsyah Kurniawana, Seng Keat Ooia & Vladan Babovic
Computers & Geosciences, Volume 72, Pages 94–104, DOI: DOI: 10.1016/j.cageo.2014.07.007, Elsevier, November 2014.
Link: http://dx.doi.org/10.1016/j.cageo.2014.07.007
Reverse engineering methodology for bioinformatics based on genetic programming, differential expression analysis and other statistical methods
Corneliu T. C. Arsene, Denisa Ardevan and Paul Bulzu
Computational Intelligence Methods for Bioinformatics and Biostatistics, pp. 161-177, Lecture Notes in Computer Science, ISBN: 978-3-319-09041-2, Springer International Publishing, DOI: 10.1007/978-3-319-09042-9_12, 2014.
Link: http://dx.doi.org/10.1007/978-3-319-09042-9_12
Dynamic travel time prediction using data clustering and genetic programming
Mohammed Elhenawy, Hao Chen, Hesham A. Rakha
Transportation Research Part C: Emerging Technologies, Vol. 42, pp. 82-98, ISSN 0968-090X, DOI: 10.1016/j.trc.2014.02.016, Elsevier, May 2014.
Link: http://dx.doi.org/10.1016/j.trc.2014.02.016
Multigene genetic programming for estimation of elastic modulus of concrete
Alireza Mohammadi Bayazidi, Gai-Ge Wang, Hamed Bolandi, Amir H. Alavi and Amir H. Gandomi
Mathematical Problems in Engineering, Volume 2014, Article ID 474289, DOI: 10.1155/2014/474289, 2014.
Link: http://dx.doi.org/10.1155/2014/474289
Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach
A. Garg, K. Tai & M.M. Savalani
International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s00170-014-5820-9, ISSN: 0268-3768, Springer London, April 2014.
Link: http://dx.doi.org/10.1007/s00170-014-5820-9
Classification-driven model selection approach of genetic programming in modelling of turning process
A. Garg & L. Rachmawati
The International Journal of Advanced Manufacturing Technology, 69 (5-8), pp. 1137-1151, DOI: 10.1007/s00170-013-5103-x, Springer, 2013.
Link: http://dx.doi.org/10.1007/s00170-013-5103-x
Machine learning based modeling for solid oxide fuel cells power performance prediction
M. N. Fuad & M. A. Hussain
Proceedings of the 6th International Conference on Process Systems Engineering (PSE ASIA), Kuala Lumpur, 25 - 27 June 2013.
Link: http://www.sps.utm.my/download/PSEAsia2013-04.pdf
Global solar irradiation prediction using a multi-gene genetic programming approach
Indranil Pan, Daya Shankar Pandey, and Saptarshi Das
Journal of Renewable and Sustainable Energy 5, 063129, DOI: 10.1063/1.4850495, 2013
Link: http://dx.doi.org/10.1063/1.4850495
Mathematical modelling of burr height of the drilling process using a statistical-based multi-gene genetic programming approach
A. Garg, K. Tai & M.M. Savalani
International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s00170-014-5817-4, ISSN: 0268-3768, Springer London, April 2014.
Link: http://dx.doi.org/10.1007/s00170-014-5817-4
Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach
Reza Baratia, Seyed Ali Akbar Salehi Neyshabourib, Goodarz Ahmadic
Powder Technology, Vol. 257, pp. 11-19, ISSN 0032-5910, DOI: 10.1016/j.powtec.2014.02.045, Elsevier, May 2014.
Link: http://dx.doi.org/10.1016/j.powtec.2014.02.045
CPT-based probabilistic evaluation of seismic soil liquefaction potential using multi-gene genetic programming
Pradyut Kumar Mudulia, Sarat Kumar Dasa and Subhamoy Bhattacharya
Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, Vol. 8, Issue 1, pp. 14-28, DOI:10.1080/17499518.2013.845720, 2014
Link: http://www.tandfonline.com/doi/abs/10.1080/17499518.2013.845720
Inferring transcription networks from data
Alexandru Floares and Irina Luludachi
In book: Springer Handbook of Bio-/Neuroinformatics, Nikola Kasabov (Eds.), DOI 10.1007/978-3-642-30574-0, pp. 311-236, Springer 2014.
Link: http://link.springer.com/book/10.1007%2F978-3-642-30574-0
A deterministic and symbolic regression hybrid applied to resting-state fMRI data
Ilknur Icke, Nicholas A. Allgaier, Christopher M. Danforth, Robert A. Whelan, Hugh P. Garavan, Joshua C. Bongard, IMAGEN Consortium
In book: Genetic Programming Theory and Practice XI, Riolo, Rick. Moore, Jason H. and Kotanchek, Mark (Eds.), ISBN 978-1-4939-0374-0, Springer, 2014.
Link: http://www.cs.uvm.edu/~jbongard/papers/2013_GPTP_Icke.pdf
Estimation of mechanical properties of nanomaterials using artificial intelligence methods
V. Vijayaraghavan, A. Garg, C. H. Wong and K. Tai,
Applied Physics A, Print ISSN: 0947-8396, DOI: 10.1007/s00339-013-8192-3, Springer Berlin Heidelberg, 2013.
Link: http://dx.doi.org/10.1007/s00339-013-8192-3
Application of genetic programming to predict an SI engine brake power and torque using ethanol-gasoline fuel blends
Mostafa Kiani Deh Kiani, Barat Ghobatian, Fathollah Ommi & Gholamhassan Najafi
IJNES (International Journal of Natural and Engineering Sciences), 7 (3): 007-015, 2013.
Link: http://www.nobel.gen.tr/Makaleler/IJNES-Issue%203-8bed9f0d9d1345bc8cbaf830e9d9594d.pdf
Assessment of sediment transport approaches for sand-bed rivers by means of machine learning
Vasileios Kitsikoudis, Epaminondas Sidiropoulos & Vlassios Hrissanthou
Hydrological Sciences Journal, DOI: 10.1080/02626667.2014.909599, 2014.
Link: http://dx.doi.org/10.1080/02626667.2014.909599
Optimizing thermostable enzymes production using multigene symbolic regression genetic programming
Alaa Sheta, Rania Hiary, Hossam Faris and Nazeeh Ghatasheh
World Applied Sciences Journal, Vol 22., Issue 4, pp. 485-493, DOI: 10.5829/idosi.wasj.2013.22.04.7694, 2013.
Link: http://www.researchgate.net/publication/258047794
GPF-CLASS: a genetic fuzzy model for classification
A.S. Koshiyama, T. Escovedo, D.M. Dias, M.M.B.R. Vellasco and R. Tanscheit
In proceedings of the 2013 IEEE Congress on Evolutionary Computation, Pages 3275 - 3282, 20-23 June 2013, Cancun, Mexico, E-ISBN: 978-1-4799-0452-5, Print ISBN: 978-1-4799-0453-2, DOI: 10.1109/CEC.2013.6557971, 2013.
Link: http://dx.doi.org/10.1109/CEC.2013.6557971
Improving genetic programming based symbolic regression using deterministic machine learning
Ilknur Icke and Joshua C. Bongard
In proceedings of the 2013 IEEE Congress on Evolutionary Computation, Pages 1763 - 1770, 20-23 June 2013, Cancun, Mexico, E-ISBN: 978-1-4799-0452-5, Print ISBN: 978-1-4799-0453-2, DOI: 10.1109/CEC.2013.6557774, 2013.
Link: http://dx.doi.org/10.1109/CEC.2013.6557774
A hybrid M5ʹ-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process
Garg, A., Tai, K., Lee, C. H & Savalani, M. M
Journal Of Intelligent Manufacturing, DOI: 10.1007/s10845-013-0734-1, Springer, 2013.
Link: http://link.springer.com/content/pdf/10.1007%2Fs10845-013-0734-1.pdf
Empirical analysis of model selection criteria for genetic programming in modeling of time series system
A. Garg, S. Sriram and K. Tai
In Proceedings of 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), Pages 84 - 88, Singapore, 2013.
Link: http://www.akhilgarg.net/ss501.pdf
Selection of a robust experimental design for the effective modeling of nonlinear systems using genetic programming
A. Garg and K. Tai
In Proceedings of 2013 IEEE Symposium Series on Computational Intelligence and Data mining (CIDM), Pages 293-298, Singapore, 2013.
Link: http://www.akhilgarg.net/ss497.pdf
Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils
Habib Shahnazari, Mohamed A. Shahin and Mohammad A. Tutunchian
International Journal of Civil Engineering (IJCE), Transaction B: Geotechnical Engineering, June 2013.
Link: http://ijce.iust.ac.ir/browse.php?a_id=931&sid=1&slc_lang=en
A CO2-oil minimum miscibility pressure model based on multi-gene genetic programming
Mehdi Rezaei, Mahdi Eftekhari, Mahin Schaffie and Mohammad Ranjbar
Energy, Exploration & Exploitation, Volume 31, Number 4, Pages 607-622, ISSN: 0144-5987 (Print), Multi Science Publishing, DOI: 10.1260/0144-5987.31.4.607, August 2013.
Link: http://dx.doi.org/10.1260/0144-5987.31.4.607
A new statistical correlation between shear wave velocity and penetration resistance of soils using genetic programming
GD Nayeri, DD Nayeri and K Barkhordari
The Electronic Journal of Geotechnical Engineering (EDGE), Vol. 18, Bundle K, Pages 2071 - 2078, 2013.
Link: http://www.ejge.com/2013/Ppr2013.201alr.pdf
Modeling intermolecular potential of He–F2 dimer from symmetry-adapted perturbation theory using multi-gene genetic programming
M. Amiri, M. Eftekhari, M. Dehestani and A. Tajaddini
Scientia Iranica, Volume 20, Issue 3, Pages 543-548, ISSN 1026-3098, DOI: 10.1016/j.scient.2012.12.040, June 2013.
Link: http://dx.doi.org/10.1016/j.scient.2012.12.040
Prediction of cyclic resistance ratio for silty sands and its applications in the simplified liquefaction analysis
Y. Jafarian, R. Vakili and A. Sadeghi Abdollahi
Computers and Geotechnics, Volume 52, Pages 54-62, ISSN 0266-352X, Elsevier, DOI: 10.1016/j.compgeo.2013.04.001, July 2013.
Link: http://dx.doi.org/10.1016/j.compgeo.2013.04.001
Derivation of sediment transport models for sand bed rivers from data-driven techniques
Vasileios Kitsikoudis, Epaminondas Sidiropoulos and Vlassios Hrissanthou
Chapter 11 In book: Sediment transport processes and their modelling applications, Edited by Andrew J. Manning, ISBN 978-953-51-1039-2, InTech, DOI: 10.5772/53432, March 2013.
CPT-based seismic liquefaction potential evaluation using multi-gene genetic programming approach
Pradyut Kumar Muduli and Sarat Kumar Das
Indian Geotechnical Journal, DOI: 10.1007/s40098-013-0048-4, Springer-Verlag, Print ISSN: 0971-9555, Online: ISSN: 2277-3347, March 2013.
Link: http://link.springer.com/article/10.1007/s40098-013-0048-4
Application of soft computing for prediction of pavement condition index
Habib Shahnazari, Mohammad A. Tutunchian, Mehdi Mashayekhi and Amir A. Amini
Journal of Transportation Engineering, Vol. 138, No. 12, Pages 1495 - 1506, Print ISSN: 0733-947X, Online ISSN: 1943-5436, American Society of Civil Engineers, 2012.
Link: http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.0000454
Prediction of ultimate bearing capacity of shallow foundations on cohesionless soils: an evolutionary approach
Habib Shahnazari and Mohammad A. Tutunchian
KSCE Journal of Civil Engineering, 16 (6), pp. 950 - 957, DOI: 10.1007/s12205-012-1651-0, Springer, September 2012.
Link: http://dx.doi.org/10.1007/s12205-012-1651-0
Prediction of depth of cut for single-pass laser micro-milling process using semi-analytical, ANN and GP approaches
Chinmay K. Desai and Abdulhafiz Shaikh
The International Journal of Advanced Manufacturing Technology, Vol. 60, Issues 9 - 12, Pages 865 - 882, Print ISSN 0268-3768, Online ISSN 1433-3015, DOI: 10.1007/s00170-011-3677-8, Springer-Verlag, 2012.
Link: http://dx.doi.org/10.1007/s00170-011-3677-8
Comparison of regression analysis, artificial neural network and genetic programming in handling the multicollinearity problem
A. Garg and K. Tai
Proceeding of ICMIC 2012 - International Conference on Modelling, Identification and Control, Wuhan, China, IEEE. pp. 353 - 358, 24 - 26 June 2012.
Link: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6260224
Review of genetic programming in modeling of machining processes
A. Garg and K. Tai
Proceeding of ICMIC 2012 - International Conference on Modelling, Identification and Control, Wuhan, China, IEEE, pp. 653 - 658, Print ISBN: 978-1-4673-1524-1, 24 - 26 June 2012.
Link: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6260225
Implementation of multigene symbolic regression in the sediment transport quantification problem for sand bed rivers
V. Kitsikoudis, E. Sidiropoulos and V. Hrissanthou
In proceedings of the 9th International Symposium on Ecohydraulics (ISE 2012), Vienna, 2012.
Link: http://www.ise2012.boku.ac.at/papers/14255_2.pdf
Estimating the non-linear dynamics of free-flying objects
Seungsu Kim and Aude Billard
Robotics and Autonomous Systems, 60(9), pp. 1108 -1122, Elsevier, September 2012.
Link: http://dx.doi.org/10.1016/j.robot.2012.05.022
Prediction of modified Mercalli intensity from PGA, PGV, moment magnitude, and epicentral distance using several nonlinear statistical algorithms
Diego A. Alvarez, Jorge E. Hurtado and Daniel Alveiro Bedoya-Ruíz
Journal of Seismology, Springer, ISSN: 1383 - 4649, January 21 2012.
Link: http://dx.doi.org/10.1007/s10950-012-9291-x
Determination of Manning's n for subsurface modular channel
L. C. Kee, N. A. Zakaria, T. L. Lau, C. K. Chang and A. A. Ghani
In 3rd International Conference on Managing Rivers in the 21st Century: Sustainable solutions for global crisis of flooding, pollution and water scarcity, Pages 266 - 273, Penang, Malaysia, 2012.
Link: http://redac.eng.usm.my/html/publish/2011_25.pdf
Mathematical formulation of knitted fabric spirality using genetic programming
Zeng Hai Chen, Bin Gang Xu, Zhe Ru Chi and Da Gan Feng
Textile Research Journal, Sage Publications, ISSN: 1746-7748, January 25, 2012.
Link: http://trj.sagepub.com/content/82/7/667.abstract
Improved model reduction and tuning of fractional-order PIλDμcontrollers for analytical rule extraction with genetic programming
Saptarshi Das, Indranil Pan, Shantanu Das, Amitava Gupta
ISA Transactions 51(2), pp. 237-261, Elsevier, 2012.
Link: http://dx.doi.org/10.1016/j.isatra.2011.10.004
A hybrid genetic programming – artificial neural network approach for modeling of vibratory finishing process
A. Garg and K. Tai
International Conference on Information and Intelligent Computing IPCSIT vol.18, IACSIT Press, Singapore, 2011.
Link: http://www.ipcsit.com/vol18/3-ICIIC%202011-C006.pdf
Robot Scientists
Tom Ashu, Michael Fink, Mohan Gopaladesikan, Karl Gregory, Fatima Jaafari and Qi Qi
In the Seventeenth Mathematical and Statistical Modeling Workshop for Graduate Students, Department of Mathematics, North Carolina State University, 7 – 15 July 2011.
Link: http://www.samsi.info/sites/default/files/IMSM_2011.pdf
A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems
Gandomi, Amir and Alavi, Amir
Neural Computing & Applications, 21(1), pp. 171-187, Springer London, 2011.
Link: http://dx.doi.org/10.1007/s00521-011-0734-z
A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems
Gandomi, Amir and Alavi, Amir
Neural Computing & Applications, 21(1), pp. 189-201, Springer London, 2011.
Link: http://dx.doi.org/10.1007/s00521-011-0735-y
Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: An evolutionary approach
Mohammad H. Baziar, Yaser Jafarian, Habib Shahnazari, Vahid Movahed and Mohammad Amin Tutunchian
Computers and Geosciences, Elsevier, 37(11), pp. 1883 - 1893, 2011.
Link: http://dx.doi.org/10.1016/j.cageo.2011.04.008
Symbolic macromodeling of parameterized S-parameter frequency responses
Dirk Deschrijver and Tom Dhaene
In proceedings of the 2010 IEEE 19th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), Pages 109 - 112, Print ISBN: 978-1-4244-6865-2, E-ISBN: 978-1-4244-6866-9, INSPEC Accession No.: 11664328, DOI: 10.1109/EPEPS.2010.5642558, Austin TX, 25-27 Oct. 2010.
Link: http://dx.doi.org/10.1109/EPEPS.2010.5642558
Natural selection of asphalt stiffness predictive models with genetic programming
Gopalakrishnan, K., Kim, S., Ceylan, H., and Khaitan, S. K.
Proceedings of the Artificial Neural Networks In Engineering (ANNIE) 2010 Conference, The American Society of Mechanical Engineers, Eds.: C. H. Dagli et al., St. Louis, Missouri, November 1-3, 2010.
Link: http://dx.doi.org/10.1115/1.859599.paper48
GPTIPS: an open source genetic programming toolbox for multigene symbolic regression
Searson, D.P., Leahy, D.E. & Willis, M.J.
Proceedings of the International MultiConference of Engineers and Computer Scientists 2010 (IMECS 2010), Hong Kong, 17-19 March, 2010.
Link: In Downloads and http://www.iaeng.org/publication/IMECS2010/IMECS2010_pp77-80.pdf
Evolving toxicity models using multigene symbolic regression and multiple objectives
Hii, C., Searson D.P., & Willis, M.J.
International Journal of Machine Learning and Computing, Vol.1, No. 1, ISSN: 2010-3700, IACSIT, April 2011.
Link: http://ijmlc.org/papers/05-L0037.pdf
Multi-objective genetic programming for multigene symbolic regression
Hii, C., Searson D.P. & Willis, M.J.
Proceedings of the 3rd International Conference on Machine Learning and Computing (ICMLC 20100), IEEE Catalogue Number: CFP1127J-PRT, Singapore, 26-28 Feb., 2011
Using genetic programming to evolve a team of data classifiers
Morrison, G.A., Searson, D.P. & Willis, M.J.
World Academy of Science, Engineering and Technology, Issue 72, 261-264, 2010.
Link: In Downloads