Journal Articles
2014(3 publications)
[publication]Castelli, M. and Sara Silva and Vanneschi, L. , "A C++ framework for geometric semantic genetic programming", Genetic Programming and Evolvable Machines, 2014

[publication]Castelli, M. and Sara Silva and Manzoni, L. and Vanneschi, L. , "Geometric Selective Harmony Search", Information Sciences, 2014

[publication]Vanneschi, L. and Castelli, M. and Sara Silva , "A survey of semantic methods in genetic programming", Genetic Programming and Evolvable Machines,  vol. 15,  pp. 195-214, 2014

2013(1 publication)
[publication]Moraglio, A. and Togelius, J. and Sara Silva , "Geometric Differential Evolution for Combinatorial and Programs Spaces", Evolutionary Computation Journal, to appear,  vol. 21,  pp. 591-624, 2013

2012(2 publications)
[publication]Sara Silva and Vanneschi, L. , "Bloat Free Genetic Programming: Application to Human Oral Bioavailability Prediction", International Journal of Data Mining and Bioinformatics,  vol. 6,  pp. 585-601, 2012

[publication]Sara Silva and Dignum, S. and Vanneschi, L. , "Operator equalisation for bloat free genetic programming and a survey of bloat control methods", Genetic Programming and Evolvable Machines,  vol. 13,  pp. 197-238, 2012
[citation][year=2012]Harper R (2012). Spatial Co-Evolution - Quicker, Fitter and Less Bloated. In Genetic and Evolutionary Computation Conference (GECCO 2012), 759-766.

2011(1 publication)
[publication]Sara Silva , "Reassembling Operator Equalisation – A Secret Revealed", SIGEVOlution,  vol. 5,  pp. 10-22, 2011

2009(1 publication)
[publication]Sara Silva and Costa, E. , "Dynamic Limits for Bloat Control in Genetic Programming - and a review of past and current bloat theories", Genetic Programming and Evolvable Machines,  vol. 10,  pp. 141-179, 2009
[citation][year=2012]McDermott J, White DR, Luke S, Manzoni L, Castelli M, Vanneschi L, Jaskowski W, Krawiec K, Harper R, De Jong K, O'Reilly U-M (2012). Genetic Programming Needs Better Benchmarks. In Proc of Genetic and Evolutionary Computation Conference (GECCO 2012), 791-798.
[citation][year=2012]Castelli M (2012). Measures and methods for robust genetic programming. PhD Thesis. University of Milano-Bicocca, Italy.
[citation][year=2012]Darabos C, Giacobini M, Hu T, Moore, JH (2012). Lévy-Flight Genetic Programming: Towards a New Mutation Paradigm. European Conference on Evolutionary Computation, Machine Learning and Data Mining in Computational Biology (EvoBIO), 38-49.
[citation][year=2012]Dabhi VK, Chaudhary S (2012). A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions. arXiv preprint arXiv:1211.1119.
[citation][year=2012]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.
[citation][year=2012]Ragalo AW (2012). A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming. Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), 123-129. 
[citation][year=2012]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.
[citation][year=2011]Fraser G, Arcuri A (2011). Evolutionary Generation of Whole Test Suites. In 11th International Conference on Quality Software (QSIC 2011), 31–40.
[citation][year=2011]Fraser G, Arcuri A (2011). It is not the length that matters, it is how you control it. In 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation (ICST 2011), 150–159.
[citation][year=2011]Gardner M-A, Gagné C, Parizeau M (2011). Bloat control in genetic programming with a histogram-based accept-reject method. In Proc 13th Genetic and Evolutionary Computation Conference, 187–188. 
[citation][year=2011]Helmuth T, Spector L, Martin B (2011). Size-based tournaments for node selection. In Proc 13th Genetic and Evolutionary Computation Conference, 799–802.
[citation][year=2011]Kronberger G, Fink S, Kommenda M, Affenzeller M (2011). Macro-economic Time Series Modeling and Interaction Networks. In Proc EvoApplications 2011, 101-110.
[citation][year=2011]Miller JF (2011). Cartesian Genetic Programming. Natural Computing Series. Springer.
[citation][year=2011]Poli R, Salvaris M, Cinel Caterina (2011). Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control. In Genetic Programming Theory and Practice IX, 77–95.
[citation][year=2011]Stokic I (2011). Primjena Genetskog Programiranja u Strojnom Ucenju. DIPLOMSKI RAD br. 213. Sveuciliste u Zagrebu, Fakultet Elektrotehnike I Racunarstva, Zagreb, Croatia.
[citation][year=2011]Trujillo L (2011). Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(8): 1551–1567.
[citation][year=2011]Trujillo L, Martinez Y, Melin P (2011). Estimating Classifier Performance with Genetic Programming. In Proc European Conference on Genetic Programming (EuroGP 2011), 274-285.
[citation][year=2011]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.
[citation][year=2011]Vanneschi L, Mussi L, Cagnoni S (2011). Hot topics in Evolutionary Computation. Intelligenza Artificiale 5(1): 5–17.
[citation][year=2010]HAJIRA JABEEN, ABDUL RAUF BAIG (2010). Review of Classification Using Genetic Programming. Hajira Jabeen et al. (eds). International Journal of Engineering Science and Technology, Vol.2 (2), 2010, pp. 94-103, 2010.
[citation][year=2009]Kinzett D, Johnston M, Zhang M. How Online Simplification Affects Building Blocks in Genetic Programming.
In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 979"986.

Conference Articles
2017(3 publications)
[publication]Vanneschi, L. and Castelli, M. and Ivo Gonçalves and Manzoni, L. and Sara Silva , "Geometric Semantic Genetic Programming for Biomedical Applications: A State of the Art Upgrade", in IEEE Congress on Evolutionary Computation, 2017

[publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. and Castelli, M. , "Unsure When to Stop? Ask Your Semantic Neighbors", in Genetic and Evolutionary Computation Conference, 2017

[publication]Vanneschi, L. and Castelli, M. and Manzoni, L. and Krawiec, K. and Moraglio, A. and Sara Silva and Ivo Gonçalves , "PSXO - Population-Wide Semantic Crossover", in Genetic and Evolutionary Computation Conference, 2017

2016(2 publications)
[publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. and Castelli, M. , "Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach", in Genetic and Evolutionary Computation Conference, 2016

[publication]Castelli, M. and Manzoni, L. and Ivo Gonçalves and Vanneschi, L. and Trujillo, L. and Sara Silva , "An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach", in International Conference on Evolutionary Computation Theory and Applications, 2016

2015(3 publications)
[publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "On the Generalization Ability of Geometric Semantic Genetic Programming", in 18th European Conference on Genetic Programming (EuroGP 2015), 2015
[citation][year=2015]Dick, Grant, Aysha P. Rimoni, and Peter A. Whigham. "A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem." Proceedings of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Graff, Mario, Eric Sadit Tellez, Elio Villasenor, and Sabino Miranda-Jiménez. "Semantic Genetic Programming Operators Based on Projections in the Phenotype Space."

[publication]Ivo Gonçalves and Sara Silva and Fonseca, C.M. , "Semantic Learning Machine: A Feedforward Neural Network Construction Algorithm Inspired by Geometric Semantic Genetic Programming", in 17th Portuguese Conference on Artificial Intelligence (EPIA 2015), 2015

[publication]Munoz, L. and Sara Silva and Trujillo, L. , "M3GP - Multiclass Classification with GP", in EuroGP 2015, 2015

2014(2 publications)
[publication]Ruberto, S. and Vanneschi, L. and Castelli, M. and Sara Silva , " ESAGP – A Semantic GP Framework Based on Alignment in the Error Space", in EuroGP-2014, 2014

[publication]Ingalalli, V. and Sara Silva and Castelli, M. and Vanneschi, L. , "A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems", in EuroGP-2014, 2014

2013(5 publications)
[publication]Ivo Gonçalves and Sara Silva , "Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data", in 16th European Conference on Genetic Programming (EuroGP 2013), 2013
[citation][year=2015]Medernach, D., Fitzgerald, J., Azad, R., and Ryan, C.. "Wave: Incremental Erosion of Residual Error." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Gonçalves, Eduardo C., Alexandre Plastino, and Alex A. Freitas. "Simpler is Better: a Novel Genetic Algorithm to Induce Compact Multi-label Chain Classifiers." Proceedings of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." arXiv preprint arXiv:1504.08168 (2015).
[citation][year=2014]Fitzgerald, Jeannie. "Bias and Variance Reduction Strategies for Improving Generalisation Performance of Genetic Programming on Binary Classification Tasks." PhD diss., University of Limerick, 2014.
[citation][year=2014]Azad, R., David Medernach, and Conor Ryan. "Efficient interleaved sampling of training data in genetic programming." In Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion, pp. 127-128. ACM, 2014.
[citation][year=2014]Muhammad Atif Azad, R., David Medernach, and Conor Ryan. "Efficient approaches to interleaved sampling of training data for symbolic regression." In Nature and Biologically Inspired Computing (NaBIC), 2014 Sixth World Congress on, pp. 176-183. IEEE, 2014.
[citation][year=2014]Goldstein, Evan B., and Giovanni Coco. "A machine learning approach for the prediction of settling velocity." Water Resources Research (2014).
[citation][year=2014]Martínez, Yuliana, Leonardo Trujillo, Enrique Naredo, and Pierrick Legrand. "A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming." EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (2014): 201-212.
[citation][year=2013]Gonçalves, Eduardo Corrêa, Alexandre Plastino, and Alex A. Freitas. "A Genetic Algorithm for Optimizing the Label Ordering in Multi-Label Classifier Chains." Proceedings of the 2013 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE Computer Society Conference Publishing Services (CPS), 2013.
[citation][year=2013]Goldstein, E. B., G. Coco, A. B. Murray, and M. O. Green. "Data driven components in a model of inner shelf sorted bedforms: a new hybrid model." Earth Surface Dynamics Discussions 1, no. 1 (2013): 531-569.

[publication]Sara Silva and Ingalalli, V. and Vinga, S. and Carreiras, J.M.B. and Melo, J. and Castelli, M. and Vanneschi, L. and Ivo Gonçalves and Caldas, J. , "Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting", in EvoApplications, 2013
[citation][year=2014]Filippi, Anthony M., ?nci Güneralp, and Jarom Randall. "Hyperspectral remote sensing of aboveground biomass on a river meander bend using multivariate adaptive regression splines and stochastic gradient boosting." Remote Sensing Letters 5.5 (2014): 432-441.
[citation][year=2014]Güneralp, ?nci, Anthony M. Filippi, and Jarom Randall. "Estimation of floodplain aboveground biomass using multispectral remote sensing and nonparametric modeling." International Journal of Applied Earth Observation and Geoinformation 33 (2014): 119-126.

[publication]Vanneschi, L. and Castelli, M. and Manzoni, L. and Sara Silva , "A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics", in EuroGP-2013, 2013

[publication]Castelli, M. and Sara Silva and Vanneschi, L. and Cabral, A. and Vasconcelos, M. and Catarino, L. and Carreiras, J.M.B. , "Land Cover/Land Use Multiclass Classification Using GP With Geometric Semantic Operators", in EvoApplications-2013, 2013

[publication]Castelli, M. and Castaldi, D. and Giordani, I. and Sara Silva and Vanneschi, L. and Archetti, F. and Maccagnola, D. , "An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics", in EPIA 2013, 2013

2012(1 publication)
[publication]Ivo Gonçalves and Sara Silva and Melo, J. and Carreiras, J.M.B. , "Random Sampling Technique for Overfitting Control in Genetic Programming", in 15th European Conference on Genetic Programming (EuroGP 2012), 2012
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Garg, Akhil, and Kang Tai. "Evolving genetic programming models of higher generalization ability in modelling of turning process." Engineering Computations 32, no. 8 (2015)
[citation][year=2015]Kommenda, Michael, Michael Affenzeller, Gabriel Kronberger, Bogdan Burlacu, and Stephan Winkler. "Multi-Population Genetic Programming with Data Migration for Symbolic Regression." In Computational Intelligence and Efficiency in Engineering Systems, pp. 75-87. Springer International Publishing, 2015.
[citation][year=2015]Villar, José R., A. Enrique, Javier Sedano, and Marco A. García Tamargo. "Simple heuristics for enhancing GP learning." Logic Journal of IGPL (2015): jzv003.
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." arXiv preprint arXiv:1504.08168 (2015).
[citation][year=2014]Garg, Akhil, Ankit Garg, K. Tai, and S. Sreedeep. "An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes." Engineering Applications of Artificial Intelligence (2014).
[citation][year=2014]Fitzgerald, Jeannie. "Bias and Variance Reduction Strategies for Improving Generalisation Performance of Genetic Programming on Binary Classification Tasks." PhD diss., University of Limerick, 2014.
[citation][year=2014]Garg, A., K. Tai, and M. M. Savalani. "Formulation of bead width model of an SLM prototype using modified multi-gene genetic programming approach." The International Journal of Advanced Manufacturing Technology (2014): 1-14.
[citation][year=2014]Haeri, Maryam Amir, Mohammad Mehdi Ebadzadeh, and Gianluigi Folino. "Improving GP generalization: a variance-based layered learning approach." Genetic Programming and Evolvable Machines: 1-29.
[citation][year=2014]Garg, Ankit, Akhil Garg, K. Tai, S. Barontini, and A. Stokes. "A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves." Transport in Porous Media (2014): 1-17.
[citation][year=2014]Garg, Akhil, and Kang Tai. "An Improved Multi-Gene Genetic Programming Approach for the Evolution of Generalized Model in Modelling of Rapid Prototyping Process." In Modern Advances in Applied Intelligence, pp. 218-226. Springer International Publishing, 2014.
[citation][year=2014]Kommenda, Michael, Michael Affenzeller, Bogdan Burlacu, Gabriel Kronberger, and Stephan M. Winkler. "Genetic programming with data migration for symbolic regression." In Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion, pp. 1361-1366. ACM, 2014.
[citation][year=2013]Fitzgerald, Jeannie, R. Azad, and Conor Ryan. "A bootstrapping approach to reduce over-fitting in genetic programming." In Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, pp. 1113-1120. ACM, 2013.
[citation][year=2013]Park, Namyong, Kangil Kim, and R. I. McKay. "Cutting evaluation costs: An investigation into early termination in genetic programming." In Evolutionary Computation (CEC), 2013 IEEE Congress on, pp. 3291-3298. IEEE, 2013.
[citation][year=2013]Kommenda, Michael, Gabriel Kronberger, Stephan Winkler, Michael Affenzeller, and Stefan Wagner. "Effects of constant optimization by nonlinear least squares minimization in symbolic regression." In Proceeding of the fifteenth annual conference companion on Genetic and evolutionary computation conference companion, pp. 1121-1128. ACM, 2013.
[citation][year=2013]Goldstein, E. B., G. Coco, A. B. Murray, and M. O. Green. "Data driven components in a model of inner shelf sorted bedforms: a new hybrid model." Earth Surface Dynamics Discussions 1, no. 1 (2013): 531-569.
[citation][year=2013]Goldstein, Evan B., Giovanni Coco, and A. Brad Murray. "Prediction of wave ripple characteristics using genetic programming." Continental Shelf Research (2013).

2011(7 publications)
[publication]Ivo Gonçalves and Sara Silva , "Experiments on Controlling Overfitting in Genetic Programming", in Local proceedings of the 15th Portuguese Conference on Artificial Intelligence (EPIA 2011), 2011
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. ACM, 2015.
[citation][year=2015]Søren Atmakuri Davidsen, E. Sreedevi, and M. Padmavathamma. "Local and Global Genetic Fuzzy Pattern Classifiers." Machine Learning and Data Mining in Pattern Recognition
[citation][year=2015]Žegklitz, Jan, and Petr Pošík. "Model Selection and Overfitting in Genetic Programming: Empirical Study." arXiv preprint arXiv:1504.08168 (2015).
[citation][year=2014]Fitzgerald, Jeannie. "Bias and Variance Reduction Strategies for Improving Generalisation Performance of Genetic Programming on Binary Classification Tasks." PhD diss., University of Limerick, 2014.
[citation][year=2014]Urbano, Paulo, Enrique Naredo, and Leonardo Trujillo. "Generalization in Maze Navigation Using Grammatical Evolution and Novelty Search." In Theory and Practice of Natural Computing, pp. 35-46. Springer International Publishing, 2014.
[citation][year=2014]Martínez, Yuliana, Leonardo Trujillo, Enrique Naredo, and Pierrick Legrand. "A Comparison of Fitness-Case Sampling Methods for Symbolic Regression with Genetic Programming." EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V (2014): 201-212.
[citation][year=2012]Martinez-Arellano, Giovanna, Lars Nolle, and John Bland. "Improving WRF-ARW Wind Speed Predictions using Genetic Programming." In Research and Development in Intelligent Systems XXIX, pp. 347-360. Springer London, 2012.

[publication]Moraglio, A. and Sara Silva , "Geometric Nelder-Mead Algorithm on the Space of Genetic Programs", in Genetic and Evolutionary Computation Conference, 2011

[publication]Sara Silva , "Handling Bloat in GP, 2nd edition", in Genetic and Evolutionary Computation Conference Tutorials, 2011

[publication]Sara Silva , "Reassembling Operator Equalisation - A Secret Revealed", in Genetic and Evolutionary Computation Conference, 2011
[citation][year=2012]Harper R (2012). Spatial Co-Evolution - Quicker, Fitter and Less Bloated. In Genetic and Evolutionary Computation Conference (GECCO 2012), 759-766.

[publication]Sara Silva and Anunciação, O. and Lotz, M. , "A Comparison of Machine Learning Methods for the Prediction of Breast Cancer", in European Conference on Evolutionary Computation, Machine Learning and Data Mining in Computational Biology, 2011

[publication]Manzoni, L. and Castelli, M. and Sara Silva and Vanneschi, L. , "A Quantitative Study of Learning and Generalization in Genetic Programming", in European Conference on Genetic Programming, 2011
[citation][year=2011]Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.
[citation][year=2011]Ni J, Drieberg RH, Rockett PI (2011). The Use of an Analytic Quotient Operator in Genetic Programming. IEEE Transactions on Evolutionary Computation, to appear.

[publication]Trujillo, L. and Sara Silva and Legrand, P. and Vanneschi, L. , "An empirical study of functional complexity as an indicator of overfitting in Genetic Programming", in European Conference on Genetic Programming, 2011
[citation][year=2012]Dabhi VK, Chaudhary S (2012). A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions. arXiv preprint arXiv:1211.1119.

2010(6 publications)
[publication]Moraglio, A. and Sara Silva , "Geometric Differential Evolution on the Space of Genetic Programs", in 13th European Conference on Genetic Programming (EuroGP-2010), 2010
[citation][year=2010]R. Poli, L. Vaneschi, W.B. Langdon and N. McPhee, Theoretical results in gentic programming: the next ten years?, Genetic Programming and Evolvable Machines, Volume 11, Nº 3-4,pp.285-320, 2010.

[publication]Sara Silva and Vanneschi, L. , "State-of-the-art Genetic Programming for Predicting Human Oral Bioavailability of Drugs", in 4th International Workshop on Practical Applications of Computational Biology & Bioinformatics (IWPACBB), 2010

[publication]Sara Silva and Vasconcelos, M. and Melo, J. , "Bloat Free Genetic Programming versus Classification Trees for Identification of Burned Areas in Satelitte Imagery", in EvoApplications 2010 (EvoIASP-2010), 2010

[publication]Lotz, M. and Sara Silva , "Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions", in EvoApplications 2010 (EvoEnvironment-2010), 2010
[citation][year=2011]Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.


[publication]Vanneschi, L. and Castelli, M. and Sara Silva , "Measuring Bloat, Overfitting and Functional Complexity in Genetic Programming", in 2010 Genetic and Evolutionary Computation Conference (GECCO 2010), 2010
[citation][year=2012]Bassett JK, Kamath U, De Jong, KA (2012). A new methodology for the GP theory toolbox. In Proc Genetic and Evolutionary Computation Conference (GECCO 2012), 719-726.
[citation][year=2012]Kamath U, Bassett JK, De Jong, KA (2012). Using Quantitative Genetics and Phenotypic Traits in Genetic Programming. http://dx.doi.org/10.5772/50143
[citation][year=2012]Dabhi VK, Chaudhary S (2012). A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions. arXiv preprint arXiv:1211.1119.
[citation][year=2012]Nguyen TH, Nguyen XH, McKay B, Nguyen QU (2012). Where should we stop? an investigation on early stopping for GP learning. Proceedings of the 9th international conference on Simulated Evolution and Learning (SEAL-2012), 391-399.
[citation][year=2011]Kronberger G, Kommenda M, Affenzeller M (2011). Overfitting detection and adaptive covariant parsimony pressure for symbolic regression. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 631–638.
[citation][year=2011]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.
[citation][year=2011]Trujillo L, Martinez Y, Melin P (2011). Estimating Classifier Performance with Genetic Programming. In Proc European Conference on Genetic Programming (EuroGP 2011), 274-285.
[citation][year=2011]Fitzgerald J, Ryan C (2011). Validation Sets for Evolutionary Curtailment with Improved Generalisation. In Convergence and Hybrid Information Technology, 282-289.
[citation][year=2011]Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.
[citation][year=2011]Hien NT, Hoai NX, McKay B (2011). A Study on Genetic Programming with Layered Learning and Incremental Sampling. In IEEE Congress on Evolutionary Computation (CEC 2011), 1179 – 1185.
[citation][year=2011]Azad RMA, Ryan C (2011). Variance based selection to improve test set performance in genetic programming. In Genetic and Evolutionary Computation Conference (GECCO 2011), 1315-1322.


[publication]Castelli, M. and Manzoni, L. and Sara Silva and Vanneschi, L. , "A Comparison of the Generalization Ability of Different Genetic Programming Frameworks", in IEEE Congress on Evolutionary Computation 2010, 2010
[citation][year=2011]Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.
[citation][year=2011]Azad RMA, Ryan C (2011). Variance based selection to improve test set performance in genetic programming. In Genetic and Evolutionary Computation Conference (GECCO 2011), 1315-1322.


2009(3 publications)
[publication]Sara Silva and Dignum, S. , "Extending Operator Equalisation: Fitness Based Self Adaptive Length Distribution for Bloat Free GP", in 12th European Conference on Genetic Programming (EuroGP-2009), 2009
[citation][year=2013]Nguyen QU, Nguyen XH, O'Neill M, McKay RI, Phong DN (2013). On the roles of semantic locality of crossover in genetic programming. Information Sciences, 10.1016/j.ins.2013.02.008.
[citation][year=2012]Quang Uy N, Xuan Hoai N, O'Neill M, McKay RI, Phonge DN (2012). On the roles of semantic locality of crossover in genetic programming. Information Sciences.
[citation][year=2011]Kronberger G, Kommenda M, Affenzeller M (2011). Overfitting detection and adaptive covariant parsimony pressure for symbolic regression. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 631–638.
[citation][year=2011]Nguyen QU (2011). Examining Semantic Diversity and Semantic Locality of Operators in Genetic Programming. PhD Thesis. School of Computer Science and Informatics, University College Dublin.
[citation][year=2011]Spector L (2011). Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems. In Genetic Programming Theory and Practice VIII, 17-33.
[citation][year=2011]Trujillo L (2011). Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(8): 1551–1567.

[citation][year=2010]Poli R, Vanneschi L, Langdon WB, McPhee NF (2010). Theoretical results in genetic programming: the next ten years? Genetic Programming and Evolvable Machines 11(3-4): 285-320.
[citation][year=2010]Nguyen QU, McKay B, O"Neill M, Nguyen XH (2010). Self-Adapting Semantic Sensitivities for Semantic Similarity Based Crossover. CEC 2010, pp. 4034-4040.
[citation][year=2010]Dignum S, Poli R (2010). Sub-tree Swapping Crossover and Arity Histogram Distributions. In Proc 13th European Conference on Genetic Programming (EuroGP-2010), 38-49.

[publication]Sara Silva and Vanneschi, L. , "Operator Equalisation, Bloat and Overfitting - A Study on Human Oral Bioavailability Prediction", in 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), 2009
[citation][year=2012]Harper R (2012). Spatial Co-Evolution - Quicker, Fitter and Less Bloated. In Genetic and Evolutionary Computation Conference (GECCO 2012), 759-766.
[citation][year=2012]Nguyen TH, Nguyen XH, McKay B, Nguyen QU (2012). Where should we stop? an investigation on early stopping for GP learning. Proceedings of the 9th international conference on Simulated Evolution and Learning (SEAL-2012), 391-399.
[citation][year=2011]Ahn EY, Mullen T, Yen J (2011). Evolutionary based feature extraction with dynamic mutation. In Proc IEEE 2011 Congress on Evolutionary Computation, 409–416.
[citation][year=2011]Ahn EY, Mullen T, Yen J (2011). A two-population evolutionary algorithm for feature extraction: Combining filter and wrapper. In Proc IEEE 2011 Congress on Evolutionary Computation, 736–743.
[citation][year=2011]Gardner M-A, Gagné C, Parizeau M (2011). Bloat control in genetic programming with a histogram-based accept-reject method. In Proc 13th Genetic and Evolutionary Computation Conference, 187–188. 
[citation][year=2011]Kronberger G, Kommenda M, Affenzeller M (2011). Overfitting detection and adaptive covariant parsimony pressure for symbolic regression. In Proc Genetic and Evolutionary Computation Conference (GECCO 2011), 631–638.


[publication]Vanneschi, L. and Sara Silva , "Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming", in Fourteenth Portuguese Conference on Artificial Intelligence (EPIA-2009), Artificial Life and Evolutionary Algorithms (ALEA) track, 2009

2008(1 publication)
[publication]Sara Silva and Tseng, Y. , "Classification of Seafloor Habitats using Genetic Programming", in EvoStar 2008,  vol. 4974, 2008
[citation][year=2011]Harwerth M (2011). Experiments on Islands. In Proc European Conference on Genetic Programming (EuroGP 2011), 239-249.

[citation][year=2010]Espejo PG, Ventura S, Herrera F (2010). A Survey on the Application of Genetic Programming to Classification. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 40(2): 121-144.

2006(1 publication)
[publication]Fayolle, P. and Sara Silva and Latinier, G. and Saffrey, D. and Rosenberger, C. and Toinard, C. , "Shape Modeling With Genetic Programming", in 8th International Conference on Virtual Reality, 2006

2005(6 publications)
[publication]Sara Silva and Costa, E. , "Resource-Limited Genetic Programming: The Dynamic Approach", in Genetic and Evolutionary Computation Conference (GECCO-2005), 2005
[citation][year=2010]Whigham PA, Dick G (2010). Implicitly Controlling Bloat in Genetic Programming. IEEE Transactions on Evolutionary Computation, 14(2): 173--190.
[citation][year=2010]Costa EO, Pozo A, Vergilio SR (2010). A Genetic Programming Approach for Software Reliability Modeling. IEEE Transactions on Reliability, 59(1): 222--230.
[citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39.
[citation][year=2009]Seo K, Hyun S, Goodman ED (2009). Tree-structure-aware GP operators for automatic gait generation of quadruped robot. GECCO-2009, pp. 2155-2160.
[citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
[citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1�6.
[citation][year=2008]Kuyucu T, Trefzer M, Greensted A, Miller J, Tyrrell A (2008). Fitness functions for the unconstrained evolution of digital circuits. In 9th IEEE World Congress on Computational Intelligence (WCCI 2008), 2589-2596.
[citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.
[citation][year=2008]Chu D, Rowe JE (2008). Crossover operators to control size growth in linear GP and variable length GAs. 2008 IEEE Congress on Evolutionary Computation, vols 1-8: 336-343.
[citation][year=2007]Clifton M, Fang G (2007). Genetic Programming in Robot Exploration. In Proc 2007 IEEE International Conference on Mechatronics and Automation (ICMA 2007), 451"456.
[citation][year=2006]Luke S, Panait L (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation 14(3): 309-344.
[citation][year=2006]Costa EO, Pozo A (2006). A (mu + lambda) GP Algorithm and its use for Regression Problems. In Proc 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2006), 10-17.
[citation][year=2006]Costa EO (2006). Proposta de um Algoritmo de Programacao Genetica Baseado em Estrategias Evolucionarias. MSc Thesis, Universidade Federal do Parana, Curitiba, Brasil.
[citation][year=2006]Costa EO, Pozo A (2006). A new approach to genetic programming based on evolution strategies. In Proc 2006 IEEE International Conference on Systems, Man, and Cybernetics, 4832-4837.

[publication]Sara Silva and Silva, P. and Silva, P. and Costa, E. , "Resource-Limited Genetic Programming: Replacing Tree Depth Limits", in 7th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA05), 2005
[citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK.
[citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
[citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1"6.
[citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.
[citation][year=2008]Chu D, Rowe JE (2008). Crossover operators to control size growth in linear GP and variable length GAs. 2008 IEEE Congress on Evolutionary Computation, vols 1-8: 336-343.
[citation][year=2006]Da Costa LE, Landry JA. Relaxed genetic programming. In Proc Genetic and Evolutionary Computation Conference (GECCO"2006), 937"938.
[citation][year=2006]Costa EO (2006). Proposta de um Algoritmo de Programação Genética Baseado em Estratégias Evolucionárias. MSc Thesis, Universidade Federal do Paraná, Curitiba, Brasil.

[publication]Sara Silva and Costa, E. , "Comparing Tree Depth Limits and Resource-Limited GP", in 2005 IEEE Congress on Evolutionary Computation, 2005
[citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39.
[citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK.
[citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
[citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.

[publication]Sara Silva and Fayolle, P. and Vincent, J. and Pauron, G. and Rosenberger, C. and Toinard, C. , "Evolutionary Computation Approaches for Shape Modelling and Fitting", in Portuguese Conference on Artificial Intelligence (EPIA-2005), 2005

[publication]Sara Silva and Tseng, Y. , "Classification of Seafloor Habitats  using Genetic Programming", in Late Breaking Papers at Genetic and Evolutionary Computation Conference (GECCO-2005), 2005
[citation][year=2010]Pedro G. Espejo, Sebastián Ventura, and Francisco Herrera (2010). A Survey on the Application of Genetic Programming to Classification. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS"PART C: APPLICATIONS AND REVIEWS, VOL. 40, NO. 2, MARCH 2010.
[citation][year=2009]Griffin JM, Chen X (2009). Multiple classification of the acoustic emission signals extracted during burn and chatter anomalies using genetic programming. International journal of advanced manufacturing technology, 45(11-12): 1152-1168.
[citation][year=2009]Imada J (2009). Evolutionary Synthesis of Stochastic Gene Network Models using Feature-based Search Spaces. Technical Report # CS-09-10, Department of Computer Science, Brock University.
[citation][year=2008]Imada JH, Ross BJ (2008). Using feature-based fitness evaluation in symbolic regression with added noise. In GECCO 2008 Late Breaking Papers, 2153"2158.
[citation][year=2008]Kruss A, Blondel P, Tegowski J, Wiktor J, Tatarek A (2008). Estimation of macrophytes using single-beam and
multibeam echosounding for environmental monitoring
of arctic fjords (Kongsfjord, West Svalbard Island). In Proc of Acoustics'08 Paris, 1743"1748.
[citation][year=2007]Holmes KW, Radford B, Van Niel KP (2007). Marine versus terrestrial predictive mapping: Geographic modelling constraints of working underwater. In Proc of the 9th International Conference on Geocomputation 2007.

[publication]Tseng, Y. and Gavrilov, A. and Duncan, A. and Harwerth, M. and Sara Silva , "Implementation of genetic programming toward the improvement of acoustic classification performance for different seafloor habitats", in IEEE Oceans\'05 Europe, 2005

2004(1 publication)
[publication]Sara Silva and Costa, E. , "Dynamic Limits for Bloat Control - Variations on Size and Depth", in Genetic and Evolutionary Computation Conference (GECCO-2004), 2004
[citation][year=2011]Neshatian K, Zhang M (2011). Using genetic programming for context-sensitive feature scoring in classification problems. Connection Science 23(3): 183-207.
[citation][year=2011]Bhardwaj A, Sakalle A, Chouhan H, Bhardwaj H (2011). Controlling the problem of bloating using stepwise crossover and double mutation technique. Advanced Computing: An International Journal (ACIJ), 2(6): 59–68.
[citation][year=2011]Selle B, Muttil N (2011). Testing the structure of a hydrological model using Genetic Programming. Journal of Hydrology 397(1–2): 1–9.

[citation][year=2010]Beikia M, Basharib A, Majdia A (2010). Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International Journal of Rock Mechanics and Mining Sciences, 47(7): 1091--1103.
[citation][year=2009]Amil NM, Bredeche N, Gagné C, Gelly S, Schoenauer M, Teytaud O (2009). A Statistical Learning Perspective of Genetic Programming. In Proc 12th European Conference on Genetic Programming (EuroGP) at EvoStar 2009, 327"338.
[citation][year=2009]Kumar R, Bal BK, Rockett PI (2009). Multiobjective Genetic Programming Approach to Evolving Heuristics for the Bounded Diameter Minimum Spanning Tree Problem. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 309"316.
[citation][year=2009]Neshatian K, Zhang M (2009). Pareto Front Feature Selection: Using Genetic Programming to Explore Feature Space. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 1027"1034.
[citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39.
[citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
[citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1"6.
[citation][year=2008]Neshatian K,  Zhang M (2008). Genetic Programming and Class-Wise Orthogonal Transformation for Dimension Reduction in Classification Problems. In Proc 11th European Conference on Genetic Programming (EuroGP) at EvoStar 2008, 242"253.
[citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.
[citation][year=2007]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.
[citation][year=2007]Parasuramana K, Elshorbagya A, Sib BC (2007). Estimating Saturated Hydraulic Conductivity Using Genetic Programming. Soil Science Society America Journal, 71: 1676"1684.
[citation][year=2007]Parasuraman K (2007). Hydrologic Prediction Using Pattern Recognition and Soft-Computing Techniques. PhD Thesis, Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, Canada.
[citation][year=2006]Luke S, Panait L (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation 14(3): 309"344.
[citation][year=2006]Gelly S, Teytaud O, Bredeche N, Schoenauer M (2006). Universal Consistency and Bloat in GP. Revue d"Intelligence Artificielle 20(6): 805"827.
[citation][year=2006]Shuhua Z, Qian G, Jianguo S (2006). Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining. Journal of China University of Geosciences 17(4): 361"366.

2003(2 publications)
[publication]Sara Silva and Almeida, J. , "Dynamic Maximum Tree Depth - A Simple Technique for Avoiding Bloat in Tree-Based GP", in Genetic and Evolutionary Computation Conference (GECCO-2003), 2003
[citation][year=2013]Havlicek V, Hanel M, Maca P, Kuraz M, Pech P (2013). Incorporating basic hydrological concepts into genetic programming for rainfall-runoff forecasting. Computing. DOI 10.1007/s00607-013-0298-0

[citation][year=2012]Allaeys F (2012). Ontwikkeling van evolutionaire compacte robotsturingen en hun morfologie. MSc Thesis. University of Ghent, Belgium.
[citation][year=2012]Ragalo AW (2012). A building block conservation and extension mechanism for improved performance in Polynomial Symbolic Regression tree-based Genetic Programming. Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), 123-129. 
[citation][year=2011]Ni J, Drieberg RH, Rockett PI (2011). The Use of an Analytic Quotient Operator in Genetic Programming. IEEE Transactions on Evolutionary Computation, to appear.

[citation][year=2010]Badran K, Rockett PI (2010). The influence of mutation on population dynamics in multiobjective genetic programming. Genetic Programming and Evolvable Machines 11(1): 5-33.
[citation][year=2010]Trujillo L, Legrand P, Lévy-Véhel J (2010). The estimation of holderian regularity using genetic programming. GECCO-2010, pp. 861-868, ACM 2010.
[citation][year=2010]Beikia M, Basharib A, Majdia A (2010). Genetic programming approach for estimating the deformation modulus of rock mass using sensitivity analysis by neural network. International Journal of Rock Mechanics and Mining Sciences, 47(7): 1091--1103.
[citation][year=2010]Whigham PA, Dick G (2010). Implicitly Controlling Bloat in Genetic Programming. IEEE Transactions on Evolutionary Computation, 14(2): 173--190.
[citation][year=2009]Amil NM, Bredeche N, Gagné C, Gelly S, Schoenauer M, Teytaud O (2009). A Statistical Learning Perspective of Genetic Programming. In Proc 12th European Conference on Genetic Programming (EuroGP) at EvoStar 2009, 327"338.
[citation][year=2009]Liu K-H, Xu C-G (2009). A genetic programming-based approach to the classification of multiclass microarray datasets. Bioinformatics 2009 25(3):331"337.
[citation][year=2009]Kouchakpour P, Zaknich A, Braunl T (2009). A survey and taxonomy of performance improvement of canonical genetic programming. Knowledge and Information Systems 21(1): 1-39.
[citation][year=2009]Kumar R, Bal BK, Rockett PI (2009). Multiobjective Genetic Programming Approach to Evolving Heuristics for the Bounded Diameter Minimum Spanning Tree Problem. In Proc Genetic and Evolutionary Computation Conference (GECCO 2009), 309"316.
[citation][year=2009]Soleimani-B H, Lucas C (2009). Input Variable Generation for Long Term Prediction of EEG Signal Using Genetic Programming. Proceedings of the Seventh International Conference on Computer Science and Information Technologies (CSIT 2009), 239-242.
[citation][year=2009]Xu C-G, Liu K-H, Huang D-S (2009). The analysis of microarray datasets using a genetic programming. Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology, 176-181. 
[citation][year=2009]Zhang L, Nandi AK (2009). Diversity-preserving non-destructive operators in genetic programming and their application to breast cancer diagnosis. Transactions of the Institute of Measurement and Control 31(6): 533-550.
[citation][year=2009]White DR (2009). Genetic programming for low-resource systems. PhD Thesis, University of York, UK.
[citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK.
[citation][year=2008]Poli R, Langdon WB, McPhee NF (2008). A Field Guide to Genetic Programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk (With contributions by J.R. Koza).
[citation][year=2008]William E, Northern J (2008). Genetic Programming Lab (GPLab) Tool Set Version 3.0. In Proc 2008 IEEE Region 5 Conference, 1"6.
[citation][year=2008]Luerssen MH, Powers DMW (2008). Evolving encapsulated programs as shared grammars. Genetic Programming and Evolvable Machines, 9(3): 203"228.
[citation][year=2008]Poli R,  McPhee NF, Vanneschi L (2008). The impact of population size on code growth in GP: analysis and empirical validation. In Proc Genetic and Evolutionary Computation Conference (GECCO 2008), 1275"1282.
[citation][year=2008]Kouchakpour P (2008). Population Variation in Canonical Tree-based Genetic Programming. PhD Thesis, School of Electrical, Electronic and Computer Engineering, University of Western Australia. Nedlands, Perth, Western Australia.
[citation][year=2008]Xu C-G, Liu K-H (2008). A GP Based Approach to the Classification of Multiclass Microarray Datasets. In 	Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 340-346.
[citation][year=2007]Zhang L, Nandi AK (2007). Neutral offspring controlling operators in genetic programming. Pattern Recognition, 40(10): 2696"2705.
[citation][year=2007]Khan A, Mirza AM (2007). Genetic perceptual shaping: Utilizing cover image and conceivable attack information during watermark embedding. Information Fusion 8(4): 354"365.
[citation][year=2007]Couchet J, Manrique D, Rios J, Rodriguez-Paton A (2007). Crossover and mutation operators for grammar-guided genetic programming. Soft Computing 11(10): 943"955.
[citation][year=2007]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.
[citation][year=2007]Chaudhry A, Khan A, Ali A, Mirza AM (2007). A hybrid image restoration approach: Using fuzzy punctual kriging and genetic programming. International Journal of Imaging Systems and Technology 17(4): 224"231.
[citation][year=2007]Teytaud O (2007). Slightly Beyond Turing's Computability for Studying Genetic Programming. In Proc Fifth International Conference on Machines, Computations, and Universality, 279"290.
[citation][year=2007]Day P, Nandi AK (2007). Robust Text-Independent Speaker Verification Using Genetic Programming. IEEE Transactions on Audio, Speech, and Language Processing 15(1): 285"295.
[citation][year=2006]Sylvain Gelly, Olivier Teytaud, Nicolas Bredeche and Schoenauer M (2006). Universal Consistency and Bloat in GP. Revue d"Intelligence Artificielle 20(6): 805"827.
[citation][year=2006]Gagne C, Schoenauer M, Parizeau M, Tomassini M (2006). Genetic Programming, Validation Sets, and Parsimony Pressure. In Proc 9th European Conference on Genetic Programming (EuroGP 2006), 109"120.
[citation][year=2006]Teytaud O (2006). Why Simulation-Based Approachs with Combined Fitness are a Good Approach for Mining Spaces of Turing-equivalent functions. In Proc 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 283"290.
[citation][year=2006]Khan A, Mirza AM, Majid A (2006). Intelligent perceptual shaping of a digital watermark: Exploiting Characteristics of human visual system. International Journal of Knowledge-Based and Intelligent Engineering Systems 10(3): 213"223.
[citation][year=2006]Khan A (2006). A novel approach to decoding: Exploiting anticipated attack information using genetic programming.  International Journal of Knowledge-Based and Intelligent Engineering Systems 10(5): 337"346.
[citation][year=2006]Costa EO (2006). Proposta de um Algoritmo de Programação Genética Baseado em Estratégias Evolucionárias. MSc Thesis, Universidade Federal do Paraná, Curitiba, Brasil.
[citation][year=2006]Luke S, Panait L (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation 14(3): 309"344.
[citation][year=2006]Crane EF, McPhee NF (2006). The Effects of Size and Depth Limits on Tree Based Genetic Programming. In Genetic Programming Theory and Practice III, 223"240.
[citation][year=2006]Shuhua Z, Qian G, Jianguo S (2006). Genetic Programming Approach for Predicting Surface Subsidence Induced by Mining. Journal of China University of Geosciences 17(4): 361"366.
[citation][year=2006]Brazdilova SL (2006). Autofocusing in Automated Microscopy. RNDr Thesis, Masaryk University, Faculty of Informatics, Czech Republic.
[citation][year=2006]Zhang Y, Chen H (2006). Improved Approach of Genetic Programming and Applications for Data Mining. In Proc Second International Conference on Natural Computation, 816"819.
[citation][year=2005]Daida JM (2005). Towards Identifying Populations that Increase the Likelihood of Sucess in Genetic Programming. In Proc Genetic and Evolutionary Computation Conference (GECCO 2005), 1627"1634.
[citation][year=2005]Gelly S, Teytaud O, Bredeche N, Schoenauer M (2005). A statistical learning theory approach of bloat. In Proc Genetic and Evolutionary Computation Conference (GECCO 2005), 1783"1784.
[citation][year=2005]Luerssen MH (2005). Graph Grammar Encoding and Evolution of Automata Networks. In Proc 28th Australasian Computer Science Conference (ACSC 2005), 229"238.
[citation][year=2005]Teytaud O, Gelly S, Bredeche N, Schoenauer M (2005). Apprentissage statistique et programmation génétique: la croissance du code est-elle inévitable? Conférence d'Apprentissage (CAP 2005), 163"178.
[citation][year=2005]Washbrook B, Li J (2005). The Application of Evolutionary Computation to the Analysis of the Profiles of Elliptical Galaxies: A Maximum Likelihood Approach. In Proc 2005 IEEE Congress on Evolutionary Computation (CEC 2005), 2607"2612.
[citation][year=2005]Manrique D, Marquez F, RiosJ, Rodriguez-Paton A (2005). Grammar Based Crossover Operator in Genetic Programming. In Proc First International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC 2005), 252"261.
[citation][year=2005]Jakobovic D (2005). Rasporedivanje Zasnovano na Prilagodljivim Pravilima (Monitor-based multithreaded embedded systems). PhD Thesis, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia.
[citation][year=2004]Panait L, Luke S (2004). Alternative Bloat Control Methods. In Proc Genetic and Evolutionary Computation Conference (GECCO 2004), 630"641.
[citation][year=2004]McPhee NF, Jarvis A, Crane EF. On the Strength of Size Limits in Linear Genetic Programming. In Proc Genetic and Evolutionary Computation Conference (GECCO 2004), 593"604.
[citation][year=2004]Barlow GJ (2004). Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-objective Genetic Programming. MSc Thesis, North Carolina State University.

[publication]Sara Silva and Almeida, J. , "GPLAB - A Genetic Programming Toolbox for MATLAB", in Nordic MATLAB Conference (NMC-2003), 2003
[citation][year=2012]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.
[citation][year=2012]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.
[citation][year=2012]Olague G, Trujillo L (2012). Interest point detection through multiobjective genetic programming. Applied Soft Computing 12(8): 2566–2582.
[citation][year=2012]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.
[citation][year=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.
[citation][year=2012]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.
[citation][year=2011]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.
[citation][year=2011]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.
[citation][year=2011]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.
[citation][year=2011]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.

[citation][year=2010]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.
[citation][year=2010]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.
[citation][year=2009]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.
[citation][year=2009]Salehi-Abari A, White T (2009). The Uphill Battle of Ant Programming vs Genetic Programming. In Proceedings of ICEC-2009, pp. 171-176.
[citation][year=2009]Villegas Cortez J (2009). Sintesis Automatica de Memorias Asociativas Mediante Programacion Genetica. PhD Thesis. Centro de Investigacion en Computacion, Instituto Politecnico Nacional, Mexico.
[citation][year=2007]Parasuramana K, Elshorbagya A, Sib BC (2007). Estimating Saturated Hydraulic Conductivity Using Genetic Programming. Soil Science Society America Journal, 71: 1676"1684.
[citation][year=2007]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.
[citation][year=2007]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.
[citation][year=2007]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.
[citation][year=2007]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.
[citation][year=2006]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.
[citation][year=2006]Brazdilova SL (2006). Autofocusing in Automated Microscopy. RNDr Thesis, Masaryk University, Faculty of Informatics, Czech Republic.
[citation][year=2006]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.
[citation][year=2006]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.
[citation][year=2006]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.
[citation][year=2005]Luerssen MH (2005). Graph Grammar Encoding and Evolution of Automata Networks. In Proc 28th Australasian Computer Science Conference (ACSC 2005), 229"238.

Edited Books
2013(1 publication)
[publication]Esparcia-Alcazar, A. and Sim, K. and Sara Silva and al., e. ,Proceedings of the EvoApplications 2013 ,  vol. 7835, 2013

2012(2 publications)
[publication]Chio, C.D. and Agapitos, A. and , S.C. and Cotta, C. and Vega, F.F.d. and Caro, G.A.D. and Drechsler, R. and Esparcia-Alcazar, A. and Farooq, M. and Langdon, W. and Preuss, M. and Richter, H. and Sara Silva and Simões, A. and Squillero, G. and Tarantino, E. and Tettamanzi, A. and Togelius, J. and Urquhart, N. and Uyar, A.S. and Yannakakis, G.N. , "Applications of Evolutionary Computation: EvoApplications 2012",  vol. 7248, 2012

[publication]Moraglio, A. and Sara Silva and Krawiec, K. and Penousal Machado and Cotta, C. , "Genetic Programming - 15th European Conference, EuroGP 2012, Málaga, Spain, April 11-13, 2012. Proceedings",  vol. 7244, 2012

2011(1 publication)
[publication]Sara Silva and Foster, J.A. and Nicolau, M. and Penousal Machado and Giacobini, M. , "Genetic Programming - 14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011. Proceedings",  vol. 6621, 2011

2010(1 publication)
[publication]Esparcia-Alcazar, A. and Sara Silva and Ekart, A. and Uyar, S. and Dignum, S. , "Genetic Programming, 13th European Conference, EuroGP 2010",  vol. 6021, 2010

Book Chapters
2014(1 publication)
[publication]Vanneschi, L. and Sara Silva and Castelli, M. and Manzoni, L. , "Geometric Semantic Genetic Programming for Real Life Applications", in Genetic Programming Theory and Practice XI,  vol. 1,  pp. 191-209, 2014

2011(1 publication)
[publication]Sara Silva and Vanneschi, L. , "The Importance of Being Flat – Studying the Program Length Distributions of Operator Equalisation", in Genetic Programming Theory and Practice IX,  vol. 1,  pp. 211-233, 2011
[citation][year=2011]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.


PhD Theses
2008(1 publication)
[publication]Sara Silva , "Controlling Bloat: Individual and Population Based Approaches in Genetic Programming", 2008
[citation][year=2012]Dabhi VK, Chaudhary S (2012). A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions. arXiv preprint arXiv:1211.1119.
[citation][year=2012]Allaeys F (2012). Ontwikkeling van evolutionaire compacte robotsturingen en hun morfologie. MSc Thesis. University of Ghent, Belgium.
[citation][year=2012]Jing X, Qiu-Wang W, Min Z (2012). Improvement of Genetic Programming Symbolic Regression and its Application in Heat Exchangers. Journal of Engineering Thermophysics 33(8): 1415-1418.
[citation][year=2011]Fitzgerald J, Ryan C (2011). Validation Sets for Evolutionary Curtailment with Improved Generalisation. In Convergence and Hybrid Information Technology, 282-289.
[citation][year=2011]Hien NT, Hoai NX, McKay B (2011). A Study on Genetic Programming with Layered Learning and Incremental Sampling. In IEEE Congress on Evolutionary Computation (CEC 2011), 1179 – 1185.
[citation][year=2011]Okhovat A, Mousavi SM (2011). Modeling of arsenic, chromium and cadmium removal by nanofiltration process using genetic programming. Applied Soft Computing 12(2): 793–799. 
[citation][year=2011]Poli R (2011). Covariant Tarpeian Method for Bloat Control in Genetic Programming. In Genetic Programming Theory and Practice VIII, 71-89.
[citation][year=2011]Stokic I (2011). Primjena Genetskog Programiranja u Strojnom Ucenju. DIPLOMSKI RAD br. 213. Sveuciliste u Zagrebu, Fakultet Elektrotehnike I Racunarstva, Zagreb, Croatia.
[citation][year=2011]Trujillo L (2011). Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(8): 1551–1567.
[citation][year=2011]Xie H, Zhang M (2011). Depth-control strategies for crossover in tree-based genetic programming. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(9): 1865-1878.

[citation][year=2010]Pappa GL, Freitas AA (2010). Automating the design of data mining algorithms. An evolutionary computation approach. Springer.
[citation][year=2009]Xie H, Zhang M (2009). An Analysis and Evaluation of the Saving Capability and Feasibility of Backward-Chaining Evolutionary Algorithms. In Artificial Life: Borrowing from Biology, 63-72.
[citation][year=2009]Beadle LCJ (2009). Semantic and structural analysis of genetic programming. PhD Thesis, University of Kent, UK.

Tech Report
2010(3 publications)
[publication]Sara Silva and Dignum, S. and Vanneschi, L. , "Bloat: Past, Present, Future", 2010

[publication]Sara Silva , "Handling Bloat in GP", 2010

[publication]Sara Silva , "Genetic Programming", 2010

2005(1 publication)
[publication]Sara Silva , "Predicting Protein Secondary Structure Using Artificial Neural Networks", 2005