This paper presents GPLAB, a genetic programming toolbox for MATLAB. Besides most of the features traditionally used in genetic programming, it also implements two techniques aimed at controlling the well known bloat problem, as well as a modified version of a previously published method for automatically adapting the genetic operator probabilities in runtime, which makes it possible to use the toolbox as a test bench for new genetic operators. Combining a highly modular and adaptable structure with automatic parameterization techniques, the toolbox suits all kinds of users, from the layman who wants to use it as a "black box", to the advanced researcher who wants to build and test new functionalities. The toolbox and its documentation are freely available for download.
Keywords
genetic programming, toolbox, matlab
Subject
Genetic Programming
Conference
Nordic MATLAB Conference (NMC-2003), October 2003
PDF File
Cited by
Year 2012 : 6 citations
Kaliakatsos-Papakostas MA, Epitropakis MG, Floros A, Vrahatis MN (2012). Controlling interactive evolution of 8-bit melodies with genetic programming. Soft Computing 16:1997-2008.
Kaliakatsos-Papakostas MA, Epitropakis MG, Floros A, Vrahatis MN (2012). Interactive Evolution of 8–Bit Melodies with Genetic Programming towards Finding Aesthetic Measures for Sound. Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART), 141-152.
Olague G, Trujillo L (2012). Interest point detection through multiobjective genetic programming. Applied Soft Computing 12(8): 2566–2582.
Trujillo L, Martinez Y, Galvan-Lopez E, Legrand P (2012). A comparison of predictive measures of problem difficulty for classification with Genetic Programming. Proceedings of ERA-2012.
Garg A, Tai K (2012). Review of Genetic Programming in Modeling of Machining
Processes. Proceedings of 2012 International Conference on Modelling, Identification and Control.
Trujillo L, Legrand P, Olague G, Lévy-Véhele J (2012). Evolving estimators of the pointwise Hölder exponent with Genetic Programming. Information Sciences 209: 61–79.
Year 2011 : 4 citations
Majid A, Lee C-H, Mahmood MT, Choi T-S (2011). Impulse noise filtering based on noise-free pixels using genetic programming. In Knowledge and Information Systems, Online First.
Trujillo L, Martinez Y, Galvan-Lopez E, Legrand P (2011). Predicting problem difficulty for genetic programming applied to data classification. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 1355–1362.
Li G, Zeng X-J (2011). Genetic programming with a norm-referenced fitness function. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 1323–1330.
Noel P-L, Veeramachaneni K, O’Reilly U-M (2011). Baseline Genetic Programming: Symbolic Regression on Benchmarks for Sensory Evaluation Modeling. In Genetic Programming Theory and Practice IX, 173-194.
Year 2010 : 2 citations
Nader Keshta, Amin Elshorbagy, Lee Barbour (2010). Comparative probabilistic assessment of the hydrological performance of reconstructed and natural watersheds. Hydrological Processes 24(10): 1333-1342.
Majid A, Mahmood MT, Choi T-S (2010). Optimal composite depth function for 3D shape recovery of microscopic objects. Microscopy Research and Technique 73(7): 657-661.
Year 2009 : 3 citations
Villegas-Cortez J, Sossa JH, Avilés-Cruz C, Caballero GO (2009). Automatic Synthesis of Associative Memories by Genetic Programming, a First Approach. In Advances in Computer Science and Engineering, 91-102.
Salehi-Abari A, White T (2009). The Uphill Battle of Ant Programming vs Genetic Programming. In Proceedings of ICEC-2009, pp. 171-176.
Villegas Cortez J (2009). Sintesis Automatica de Memorias Asociativas Mediante Programacion Genetica. PhD Thesis. Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico.
Year 2007 : 5 citations
Parasuramana K, Elshorbagya A, Sib BC (2007). Estimating Saturated Hydraulic Conductivity Using Genetic Programming. Soil Science Society America Journal, 71: 1676"1684.
Brazdilova SL (2007). Towards reliable autofocusing in automated microscopy. In Proc Fourth International Conference on Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization (ICINCO 2007), 440"447.
Neshatian K, Zhang M, Johnston M (2007). Feature Construction and Dimension Reduction Using Genetic Programming. In Proc 20th Australian Conference on Artificial Intelligence, 160"170.
Holladay K, Robbins K, von Ronne J (2007). FIFTH: a stack based GP language for vector processing. In Proc 10th European Conference on Genetic Programming (EuroGP 2007), 102"113.
Burrell L, Glynn S, Vachtsevanos G, Litt B (2007). Feature Analysis of Functional MRI for Discrimination Between Normal and Epileptogenic Brain. In European Control Conference, Kos, Greece.
Year 2006 : 5 citations
Gagne C, Parizeau M (2006). Genericity in Evolutionary Computation Software Tools: Principles and Case Study. International Journal on Artificial Intelligence Tools 15(2): 173"194.
Brazdilova SL (2006). Autofocusing in Automated Microscopy. RNDr Thesis, Masaryk University, Faculty of Informatics, Czech Republic.
Arakawaa M, Hasegawab K, Funatsu K (2006). QSAR study of anti-HIV HEPT analogues based on multi-objective genetic programming and counter-propagation neural network. Chemometrics and Intelligent Laboratory Systems 83(2): 91"98.
Pazienza GE, Gomez-Ramirez E, Vilasis-Cardona X (2006). Genetic Programming for the CNN-UM. In Proc 10th International Workshop on Cellular Neural Networks and Their Applications, 1"6.
Gikandi WW (2006). Optimization Based Frameworks and Search Methodologies for the Analysis and Redesign of the Escherichia Coli Metabolic Network. MSc Thesis. Department of Chemical and Biological Engineering, State University of New York at Buffalo, USA.
Year 2005 : 1 citations
Luerssen MH (2005). Graph Grammar Encoding and Evolution of Automata Networks. In Proc 28th Australasian Computer Science Conference (ACSC 2005), 229"238.
