Abstract
The Tarpeian method for bloat control has been shown to be a robust technique to control bloat. The covariant Tarpeian method introduced last year, solves the problem of optimally setting the parameters of the method so as to achieve full control over the dynamics of mean program size. However, the theory supporting such a technique is applicable only in the case of fitness proportional selection and for a generational system with crossover only. In this paper, we propose an adaptive variant of the Tarpeian method, which does not suffer fromthis limitation. Themethod automatically adjusts the rate of application of Tarpeian bloat control so as to achieve a desired program size dynamics. We test the method in a variety of standard benchmark problems as well as in a real-world application in the field of Brain Computer Interfaces, obtaining excellent results.
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References
Alfaro-Cid, Eva,Merelo, J. J., Fernandez de Vega, Francisco, Esparcia-Alcazar, Anna I., and Sharman, Ken (2010). Bloat control operators and diversity in genetic programming: A comparative study. Evolutionary Computation, 18(2):305–332.
Allen, Sam, Burke, Edmund K., Hyde, Matthew R., and Kendall, Graham (2009). Evolving reusable 3D packing heuristics with genetic programming. In Raidl, Guenther et al., editors, GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pages 931–938, Montreal. ACM.
Burke, Edmund K., Hyde, Matthew R., Kendall, Graham, and Woodward, John (2007). Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In Thierens, Dirk et al., editors, GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, volume 2, pages 1559–1565, London. ACM Press.
Citi, L., Poli, R., Cinel, C., and Sepulveda, F. (2008). P300-based BCI mouse with genetically-optimized analogue control. IEEE transactions on neural systems and rehabilitation engineering, 16(1):51–61.
Luke, Sean and Panait, Liviu (2006). A comparison of bloat control methods for genetic programming. Evolutionary Computation, 14(3):309–344.
Mahler, S´ebastien, Robilliard, Denis, and Fonlupt, Cyril (2005). Tarpeian bloat control and generalization accuracy. In Keijzer, Maarten et al., editors, Proceedings of the 8th European Conference on Genetic Programming, volume 3447 of Lecture Notes in Computer Science, pages 203–214, Lausanne, Switzerland. Springer.
Martinez-Jaramillo, Serafin and Tsang, Edward P.K. (2009). An heterogeneous, endogenous and coevolutionary GP-based financial market. IEEE Transactionson Evolutionary Computation, 13(1):33–55.
Poli, Riccardo (2003). A simple but theoretically-motivated method to control bloat in genetic programming. In Ryan, Conor et al., editors, Genetic Programming, Proceedings of EuroGP’2003, volume 2610 of LNCS, pages 204–217, Essex. Springer-Verlag.
Poli, Riccardo (2010). Covariant tarpeian method for bloat control in genetic programming. In Riolo, Rick, McConaghy, Trent, and Vladislavleva, Ekaterina, editors, Genetic Programming Theory and Practice VIII, volume 8 of Genetic and Evolutionary Computation, chapter 5, pages 71–90. Springer, Ann Arbor, USA.
Poli, Riccardo, Langdon, William B., and McPhee, Nicholas Freitag (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).
Poli, Riccardo and McPhee, Nicholas (2008). Parsimony pressure made easy. In Keijzer, Maarten et al., editors, GECCO ’08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1267– 1274, Atlanta, GA, USA. ACM.
Poli, Riccardo and McPhee, Nicholas Freitag (2003).General schema theory for genetic programming with subtree-swapping crossover: Part II. Evolutionary Computation, 11(2):169–206.
Roberts, Mark E. and Claridge, Ela (2004). Cooperative coevolution of image feature construction and object detection. In Yao, Xin et al., editors, Parallel Problem Solving from Nature - PPSN VIII, volume 3242 of LNCS, pages 902–911, Birmingham, UK. Springer-Verlag.
Salvaris, Mathew, Cinel, Caterina, Poli, Riccardo, Citi, Luca, and Sepulveda, Francisco (2010). Exploring multiple protocols for a brain-computer interface mouse. In Proceedings of 32nd IEEE EMBS Conference, pages 4189– 4192, Buenos Aires.
Silva, Sara and Costa, Ernesto (2009). 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.
Wyns, Bart and Boullart, Luc (2009). Efficient tree traversal to reduce code growth in tree-based genetic programming. Journal of Heuristics, 15(1):77– 104.
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Poli, R., Salvaris, M., Cinel, C. (2011). Evolution of an Effective Brain-Computer Interface Mouse via Genetic Programming with Adaptive Tarpeian Bloat Control. In: Riolo, R., Vladislavleva, E., Moore, J. (eds) Genetic Programming Theory and Practice IX. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1770-5_5
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DOI: https://doi.org/10.1007/978-1-4614-1770-5_5
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