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.

This list is no longer being updated due to the high number of citations.

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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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


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.


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.


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


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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

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.


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