Random sampling technique for overfitting control in genetic programming

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7244 LNCS, Issue , 2012, Pages 218-229

  Gonçalves, Ivo ^a   Silva, Sara ^a,b   Melo, Joana B ^c   Carreiras, João M B ^c  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b INSTITUTO SUPERIOR TÉCNICO   (Portugal)

^c INSTITUTO DE INVESTIGAÇÃO CIENTÍFICA TROPICAL   (Portugal)

Author keywords

Generalization; Genetic programming; Overfitting

Indexed keywords

COMPARATIVE ANALYSIS; COMPLEX SOLUTION; DATA SETS; GENERALIZATION; MACHINE LEARNING METHODS; OVERFITTING; RANDOM SAMPLING; REAL-WORLD DATASETS; RESEARCH EFFORTS; SYMBOLIC REGRESSION;

LEARNING SYSTEMS;

GENETIC PROGRAMMING;

EID: 84859133043     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29139-5_19     Document Type: Conference Paper

References (32)

1. Poli, R., Langdon, W.B., McPhee, N.F.: A field guide to genetic programming (With contributions by J.R. Koza) (2008), http://lulu.com, http://www.gp-field-guide.org.uk
2. O'Neill, M., Vanneschi, L., Gustafson, S., Banzhaf, W.: Open Issues in Genetic Programming. Genetic Programming and Evolvable Machines 11, 339-363 (2010)
3. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press (1992)
4. Kushchu, I.: An Evaluation of Evolutionary Generalisation in Genetic Programming. Artificial Intelligence Review 18, 3-14 (2002)
5. Silva, S., Costa, E.: Dynamic Limits for Bloat Control in Genetic Programming and a review of past and current bloat theories. Genetic Programming and Evolvable Machines 10(2), 141-179 (2009)
6. Vanneschi, L., Silva, S.: Using Operator Equalisation for Prediction of Drug Toxicity with Genetic Programming. In: Lopes, L.S., Lau, N., Mariano, P., Rocha, L.M. (eds.) EPIA 2009. LNCS, vol. 5816, pp. 65-76. Springer, Heidelberg (2009)
7. Becker, L.A., Seshadri, M.: Comprehensibility and Overfitting Avoidance in Genetic Programming for Technical Trading Rules. Technical report, Worcester Polytechnic Institute (2003)
8. Mahler, S., Robilliard, D., Fonlupt, C.: Tarpeian Bloat Control and Generalization Accuracy. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 203-214. Springer, Heidelberg (2005)
9. Gagné, C., Schoenauer, M., Parizeau, M., Tomassini, M.: Genetic Programming, Validation Sets, and Parsimony Pressure. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 109-120. Springer, Heidelberg (2006)
10. Cavaretta, M.J., Chellapilla, K.: Data Mining using Genetic Programming: The implications of parsimony on generalization error. In: Proceedings of the 1999 IEEE Congress on Evolutionary Computation, pp. 1330-1337. IEEE Press (1999)
11. Zhang, B.-T., Mühlenbein, H.: Balancing Accuracy and Parsimony in Genetic Programming. Evolutionary Computation 3(1), 17-38 (1995)
12. Vladislavleva, E.J., Smits, G.F., den Hertog, D.: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming. IEEE Transactions on Evolutionary Computation 13(2), 333-349 (2009)
13. Vanneschi, L., Castelli, M., Silva, S.: Measuring Bloat, Overfitting and Functional Complexity in Genetic Programming. In: Proceedings of GECCO 2010, pp. 877-884. ACM Press (2010)
14. Trujillo, L., Silva, S., Legrand, P., Vanneschi, L.: An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds.) EuroGP 2011. LNCS, vol. 6621, pp. 262-273. Springer, Heidelberg (2011)
15. Nguyen, Q.U., Nguyen, T.H., Nguyen, X.H., O'Neill, M.: Improving the Generalisation Ability of Genetic Programming with Semantic Similarity based Crossover. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184-195. Springer, Heidelberg (2010)
16. Vanneschi, L., Gustafson, S.: Using Crossover Based Similarity Measure to Improve Genetic Programming Generalization Ability. In: Proceedings of GECCO 2009, pp. 1139-1146. ACM Press (2009)
17. Da Costa, L.E., Landry, J.-A.: Relaxed Genetic Programming. In: Proceedings of GECCO 2006, pp. 937-938. ACM Press (2006)
18. Chan, K.Y., Kwong, C.K., Chang, E.: Reducing Overfitting in Manufacturing Process Modeling using a Backward Elimination Based Genetic Programming. Applied Soft Computing 11(2), 1648-1656 (2011)
19. Nikolaev, N., de Menezes, L.M., Iba, H.: Overfitting Avoidance in Genetic Programming of Polynomials. In: Proceedings of the 2002 IEEE Congress on Evolutionary Computation, pp. 1209-1214. IEEE Press (2002)
20. Chen, S.-H., Kuo, T.-W.: Overfitting or Poor Learning: A Critique of Current Financial Applications of GP. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 34-46. Springer, Heidelberg (2003)
21. Foreman, N., Evett, M.: Preventing overfitting in GP with canary functions. In: Proceedings of GECCO 2005, pp. 1779-1780. ACM Press (2005)
22. Vanneschi, L., Rochat, D., Tomassini, M.: Multi-optimization improves genetic programming generalization ability. In: Proceedings of GECCO 2007, p. 1759. ACM Press (2007)
23. Robilliard, D., Fonlupt, C.: Backwarding: An Overfitting Control for Genetic Programming in a Remote Sensing Application. In: Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., Schoenauer, M. (eds.) EA 2001. LNCS, vol. 2310, pp. 245-254. Springer, Heidelberg (2002)
24. Banzhaf, W., Francone, F.D., Nordin, P.: The Effect of Extensive Use of the Mutation Operator on Generalization in Genetic Programming using Sparse Data Sets. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 300-309. Springer, Heidelberg (1996)
25. Archetti, F., Messina, E., Lanzeni, S., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genetic Programming and Evolvable Machines 8(4), 17-26 (2007)
26. Baccini, A., Laporte, N., Goetz, S.J., Sun, M., Dong, H.: A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters 3, 045011 (2008)
27. Lucas, R., Armston, J., Fairfax, R., Fensham, R., Accad, A., Carreiras, J., Kelley, J., Bunting, P., Clewley, D., Bray, S., Metcalfe, D., Dwyer, J., Bowen, M., Eyre, T., Laidlaw, M., Shimada, M.: An Evaluation of the ALOS PALSAR L-Band Backscatter-Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3(4), 576-593 (2010)
28. Saatchi, S.S., Harris, N.L., Brown, S., Lefsky, M., Mitchard, E.T.A., Salas, W., Zutta, B.R., Buermann, W., Lewis, S.L., Hagen, S., Petrova, S., White, L., Silman, M., Morel, A.: Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 108(24), 9899-9904 (2011)
29. Gathercole, C., Ross, P.: Dynamic Training Subset Selection for Supervised Learning in Genetic Programming. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 312-321. Springer, Heidelberg (1994)
30. Liu, Y., Khoshgoftaar, T.: Reducing Overfitting in Genetic Programming Models for Software Quality Classification. In: Proceedings of the Eighth IEEE International Symposium on High Assurance Systems Engineering, pp. 56-65. IEEE Press (2004)
31. Gonçalves, I., Silva, S.: Experiments on Controlling Overfitting in Genetic Programming. In: 15th Portuguese Conference on Artificial Intelligence (to appear)
32. Luke, S., Panait, L.: Lexicographic parsimony pressure. In: Proceedings of GECCO 2002, pp. 829-836. Morgan Kaufmann (2002)