Abstract
While successful applications have been reported using standard GP crossover, limitations of this approach have been identified by several investigators. Among the most compelling alternatives to standard GP crossover are those that use some form of homologous crossover, where code segments that are exchanged are structurally or syntactically aligned in order to preserve context and worth. This paper reports the results of an empirical comparison of GP using standard crossover methods with GP using homologous crossover methods. Ten problems are tested, five each of pattern recognition and regression.
Results suggest that in terms of generalization accuracy, homologous crossover does generate consistently better performance. In addition, there is a consistently lower fraction of introns that are generated in the solution code.
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References
W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone, Genetic Programming: On the Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann: San Francisco, CA, 1998.
M. Crosbie and E. Spafford, "Applying genetic programming to intrusion detection," in Working Notes for the AAAI Symposium on Genetic Programming, J. Seigel and J. Koza, (eds.), MIT Press: Cambridge, MA, 1995.
T. Dietterich, "Statistical tests for comparing supervised learning algorithms," Technical Report, Oregon State University, 1996.
J. A. Foster, "Review: Disciplus: a commercial genetic programming system," Genetic Programming and Evolvable Machines vol. 2, pp. 201–203, 2001.
F. Francone, W. Banzhaf, M. Conrads, and P. Nordin, "Homologous crossover in genetic programming," in Proceedings of the Genetic and Evolutional Computation Conference, Orlando, FL, 1999, pp. 1021–1026.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer-Verlag: NewY ork, 2001.
D. Hosmer and S. Lemeshow, Applied Logistic Regression, Wiley: New York, 1989.
W. B. Langdon, "Size fair and homologous tree crossovers," CWI Technical Report, Amsterdam, The Netherlands, 1999.
T. Mitchell, Machine Learning, McGraw-Hill: Boston, MA, 1997.
P. Nordin and W. Banzhaf, "Complexity compression and evolution," in Genetic Algorithms: Proceedings of the Sixth International Conference, Pittsburgh, PA, 1995, pp. 310–317.
U.-M. O'Reilly and F. Oppacher, "Hybridized crossover-based search techniques for program discovery," in Proceedings of the 1995 World Conference on Evolutionary Computation, Perth, Australia, 1995, vol. 2, p. 573.
R. Poli and W. B. Langdon, "On the ability to search the space of programs of standard, one-point and uniform crossover in genetic programming," Technical Report CSRP–98–7, School of Computer Science, The University of Birmingham, 1998.
R. Poli, "Exact schema theory for genetic programming and variable-length genetic algorithms with one-point crossover," Genetic Programming and Evolvable Machines vol. 2, pp. 123–163, 2001.
L. Prechelt, "A quantitative study of experimental evaluations of neural network algorithms: Current research practice," Neural Networks vol. 9, pp. 317–327, 1997.
R. Rasch and H. Tosi, "Factors affecting software developers' performance: An integrated approach." MIS Quarterly vol. 7, pp. 15–31, 1992.
S. Salzberg, "On comparing classifiers: pitfalls to avoid and a recommended approach," Data Mining and Knowledge Discovery vol. 1, pp. 245–263, 1997.
S. Sankoff and J. Kruskal, Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison, Addison-Wesley: Reading, MA, 1983.
T. Stamey, J. Kabalin, J. McNeal, I. Johnstone, F. Freiha, E. Redwine, and N. Yang, "Prostate specific antigen in the diagnosis and treatment of adenocarcinoma of the prostate II. Radical prostatectomy treated patients," Journal of Urology vol. 16, pp. 421–445, 1989.
A. Teller, "Evolving programmers: The co-evolution of intelligent recombination operators," Advances in Genetic Programming vol. 2, pp. 45–68, 1996.
A. Teller, Machine Learning Data Repository, University of California, Irvine, www.ics.uci.edu/~mlean/MLRepository, html
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Hansen, J.V. Genetic Programming Experiments with Standard and Homologous Crossover Methods. Genetic Programming and Evolvable Machines 4, 53–66 (2003). https://doi.org/10.1023/A:1021825110329
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DOI: https://doi.org/10.1023/A:1021825110329