Simon Harding
Jürgen Leitner
ABSTRACT
The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of Cartesian Genetic Programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.
- H. A. Abbass. An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25:265--281, 2002. Google Scholar
Digital Library
- F. Binard and A. Felty. Genetic programming with polymorphic types and higher-order functions. In Proceedings of the 10th annual conference on Genetic and evolutionary computation, GECCO '08, pages 1187--1194, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- M. Brameier and W. Banzhaf. A comparison of linear genetic programming and neural networks in medical data mining. Evolutionary Computation, IEEE Transactions on, 5(1):17 --26, feb 2001. Google ScholarDigital Library
- K. Chang and J. Ghosh. Principal curve classifier-a nonlinear approach to pattern classification. In Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, volume 1, pages 695 --700 vol.1, may 1998.Google ScholarCross Ref
- C. Clack and T. Yu. Performance enhanced genetic programming. In IN PROCEEDINGS OF THE SIXTH CONFERENCE ON EVOLUTIONARY PROGRAMMING, pages 87--100. Springer, 1997. Google ScholarDigital Library
- A. Eftekhari, H. A. Moghaddam, M. Forouzanfar, and J. Alirezaie. Incremental local linear fuzzy classifier in fisher space. EURASIP J. Adv. Signal Process, 2009:15:1--15:9, January 2009. Google ScholarDigital Library
- P. Espejo, S. Ventura, and F. Herrera. A survey on the application of genetic programming to classification. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 40(2):121 --144, march 2010. Google ScholarDigital Library
- H. A. Fayed, S. Hashem, and A. F. Atiya. Self-generating prototypes for pattern classification. Pattern Recognition, pages 1498--1509, 2007. Google ScholarDigital Library
- A. Frank and A. Asuncion. UCI machine learning repository, 2010.Google Scholar
- G. Giacinto and F. Roli. Dynamic classifier selection based on multiple classifier behaviour. Pattern Recognition, 34:1879--1881, 2001.Google ScholarCross Ref
- A. Guerin-Dugue and et al. Deliverable r3-b4-p task b4: Benchmarks. Technical report, Technical Report, 1995.Google Scholar
- P.-F. Guo, P. Bhattacharya, and N. Kharma. Automated synthesis of feature functions for pattern detection. In Electrical and Computer Engineering (CCECE), 2010 23rd Canadian Conference on, pages 1--4, may 2010.Google ScholarCross Ref
- P. Hao, L. Tsai, and M. Lin. A new support vector classification algorithm with parametric-margin model. In Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pages 420--425. IEEE, 2008.Google ScholarCross Ref
- S. Harding, J. F. Miller, and W. Banzhaf. Developments in cartesian genetic programming: self-modifying cgp. Genetic Programming and Evolvable Machines, 11(3--4):397--439, 2010. Google ScholarDigital Library
- S. Harding, J. F. Miller, and W. Banzhaf. A survey of self modifying cgp. Genetic Programming Theory and Practice, 2010, 2010.Google Scholar
- R. Janghel, A. Shukla, R. Tiwari, and R. Kala. Intelligent decision support system for breast cancer. In Y. Tan, Y. Shi, and K. Tan, editors, Advances in Swarm Intelligence, volume 6146 of Lecture Notes in Computer Science, pages 351--358. Springer Berlin / Heidelberg, 2010. Google ScholarDigital Library
- H. X. Liu, R. S. Zhang, F. Luan, X. J. Yao, M. C. Liu, Z. D. Hu, and B. T. Fan. Diagnosing breast cancer based on support vector machines. Journal of Chemical Information and Computer Sciences, 43(3):900--907, 2003.Google ScholarCross Ref
- J. F. Miller. An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO), pages 1135--1142, Orlando, Florida, 1999. Morgan Kaufmann.Google Scholar
- J. F. Miller, editor. Cartesian Genetic Programming. Natural Computing Series. Springer, 2011.Google Scholar
- J. F. Miller, D. Job, and V. K. Vassilev. Principles in the evolutionary design of digital circuits - part I. Genetic Programming and Evolvable Machines, 1(1):8--35, 2000. Google ScholarDigital Library
- J. F. Miller and S. L. Smith. Redundancy and computational efficiency in cartesian genetic programming. In IEEE Transactions on Evoluationary Computation, volume 10, pages 167--174, 2006. Google ScholarDigital Library
- D. J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):199--230, 1995. Google ScholarDigital Library
- M. Oltean and C. Grosan. Solving classification problems using infix form genetic programming. In Programming, The 5 th International Symposium on Intelligent Data Analysis, M. Berthold (et al), (Editors), LNCS 2810, pages 242--252. Springer, 2003.Google Scholar
- L. Prechelt. PROBEN1 -- A set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Fakultat für Informatik, Universität Karlsruhe, D-76128 Karlsruhe, Germany, Sep 1994.Google Scholar
- A. Robinson and L. Spector. Using genetic programming with multiple data types and automatic modularization to evolve decentralized and coordinated navigation in multi-agent systems. In E. Cantú-Paz, editor, Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002), pages 391--396, New York, NY, July 2002. AAAI.Google Scholar
- M. Schmidt and H. Lipson. Distilling free-form natural laws from experimental data. Science, 324(5923):81, 2009.Google ScholarCross Ref
- L. Spector, J. Klein, and M. Keijzer. The push3 execution stack and the evolution of control. In H.-G. Beyer, U.-M. O'Reilly, D. V. Arnold, W. Banzhaf, C. Blum, E. W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, J. A. Foster, E. D. de Jong, H. Lipson, X. Llora, S. Mancoridis, M. Pelikan, G. R. Raidl, T. Soule, A. M. Tyrrell, J.-P. Watson, and E. Zitzler, editors, GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, volume 2, pages 1689--1696, Washington DC, USA, 25--29 June 2005. ACM Press. Google ScholarDigital Library
- Wikipedia. Matthews correlation coefficient -- wikipedia, the free encyclopedia, 2011. {Online; accessed 27-January-2012}.Google Scholar
- T. Yu and C. Clack. Polygp: a polymorphic genetic programming system in haskell. In Proc. of the 3rd Annual Conf. Genetic Programming, pages 416--421. Morgan Kaufmann, 1998.Google Scholar
- B.-T. Zhang and H. Muhlenbein. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation, 3:17--38, 1995. Google ScholarDigital Library
- MT-CGP: mixed type cartesian genetic programming
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