Using Genetic Programming for Data Science: Lessons Learned

Steven Gustafson, Ram Narasimhan, Ravi Palla, Aisha Yousuf

Genetic Programming Theory and Practice XIII, pp 117-135; DOI 10.1007/978-3-319-34223-8_7

Highly Accurate Symbolic Regression with Noisy Training Data

Michael F. Korns

Genetic Programming Theory and Practice XIII, pp 91-115; DOI 10.1007/978-3-319-34223-8_6

Multi Objective Symbolic Regression

C. J. Hinde, N. Chakravorti, A. A. West

Advances in Computational Intelligence Systems, Volume 513 of the series Advances in Intelligent Systems and Computing pp 481-494; DOI: 10.1007/978-3-319-46562-3_31

Comparison of common parallel architectures for the execution of the island model and the global parallelization of evolutionary algorithms

Steffen Limmer, Dietmar Fey

Concurrency and Computation, Practice and Experience Feb 2016; DOI: 10.1002/cpe.3797

Symbolic Regression

Joseph L. Awange, Béla Paláncz

Geospatial Algebraic Computations 2016, pp 203-216

Trading Volatility Using Highly Accurate Symbolic Regression

Michael F. Korns

Handbook of Genetic Programming Applications 2015, pp 531-547

Application of evolutionary algorithm-based symbolic regression to language assessment: Toward nonlinear modeling

Vahid Aryadoust

Psychological Test and Assessment Modeling, Volume 57, 2015 (3), 301-337

Extremely Accurate Symbolic Regression for Large Feature Problems

Michael F. Korns

Genetic Programming Theory and Practice XII, Genetic and Evolutionary Computation 2015, pp 109-131

Genetic Programming

James McDermott, Una-May O'Reilly

Springer Handbook of Computational Intelligence 2015, pp 845-869

Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs

Michaela Sikulova, Jiri Hulva, Lukas Sekanina

Genetic Programming: Lecture Notes in Computer Science, Volume 9025, 2015, pp 113-125

The EvoSpace Model for Pool-Based Evolutionary Algorithms

Mario García-Valdez, Leonardo Trujillo, Juan-J Merelo, Francisco Fernández de Vega, Gustavo Olague

Journal of Grid Computing, November 2014

Computation and Scientific Discovery? A Bio-inspired Approach

Ioan Muntean


Software Intensive Science

John Symons, Jack Horner

Philosophy & Technology, May 2014

Learning Dynamical Systems Using Standard Symbolic Regression

Sébastien Gaucel, Maarten Keijzer, Evelyne Lutton, and Alberto Tonda


Fuzzy functions via genetic programming

Adil Baykasoglu, Sultan Maral

Journal of Intelligent and Fuzzy Systems, DOI 10.3233/IFS-141205

Improving Genetic Programming Based Symbolic Regression Using Deterministic Machine Learning

Icke, Ilknur, and Joshua C. Bongard.

Congress on Evolutionary Computation (CEC), 2013 IEEE. Pp 1763-1770.

Learning regression ensembles with genetic programming at scale

Veeramachaneni, K., Derby, O., Sherry, D., & O'Reilly, U. M.

In Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference (pp. 1117-1124)

Combining fitness-based search and user modeling in evolutionary robotics.

Bongard, Josh C., and Gregory S. Hornby.

Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference. ACM, (pp. 159-166), 2013.

Efficient indexing of similarity models with inequality symbolic regression.

Bartos, Tomas, Tomas Skopal, and Juraj Mosko

Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference. ACM, (pp. 901-908), 2013.

Assessing Consumer Credit Applications by a Genetic Programming Approach.

Rampone, Salvatore, Franco Frattolillo, and Federica Landolfi.

Advanced Dynamic Modeling of Economic and Social Systems Studies in Computational Intelligence Volume 448, pp 79-89 (2013)

Safe and Interpretable Machine Learning: A Methodological Review

Otte, Clemens.

Computational Intelligence in Intelligent Data Analysis: 111-122. (2012)

Knowledge Discovery through Symbolic Regression with HeuristicLab

Kronberger, Gabriel, Stefan Wagner, Michael Kommenda, Andreas Beham, Andreas Scheibenpflug, and Michael Affenzeller

Machine Learning and Knowledge Discovery in Databases (2012): 824-827.

Software review: the ECJ toolkit

White, David R.

Genetic Programming and Evolvable Machines 13, no. 1 (2012): 65-67.

Automating scientific discovery.

Savage, Neil.

Communications of the ACM 55, no. 5, pp 9-11. (2012)

Genetic programming: a tutorial introduction.

O’Reilly, Una-May.

In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, pp. 693-710. ACM, 2012.

Evolutionary Techniques for Parametric WCET Analysis

Marref, Amine.

In 12th International Workshop on Worst-Case Execution Time Analysis, edited by Tullio Vardanega, p. 103. 2012.

Automated refinement and inference of analytical models for metabolic networks.

Schmidt, Michael D., Ravishankar R. Vallabhajosyula, Jerry W. Jenkins, Jonathan E. Hood, Abhishek S. Soni, John P. Wikswo, and Hod Lipson.

Physical biology 8, no. 5 (2011): 055011.

Equity markets and computational intelligence

Abbott, Russ.

Computational Intelligence (UKCI), 2010 UK Workshop on. IEEE, 2010.

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