Abstract
In the paper a method that moderates code growth in genetic programming is presented. The addressed problem is symbolic regression. A special mutation operator is used for the simplification of programs. If every individual program in each generation is simplified, then the performance of the genetic programming system is slightly worsened. But if simplification is applied as a mutation operator, more compact solutions of the same or better accuracy can be obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Angeline, P.J.: Genetic programming and emergent intelligence. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 75–97. MIT Press, Cambridge (1994)
Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)
Ekárt, A.: Generating class descriptions of four bar linkages. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1998 Conference, pp. 42–47 (1998)
Gilbert, W.: Genes-in-pieces revisited. Science 228, 823–824 (1985)
Hooper, D.C., Flann, N.S.: Improving the accuracy and robustness of genetic programming through expression simplification. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, p. 428 (1996)
Iba, H., de Garis, H., Sato, T.: Genetic programming using a minimum description length principle. In: Kinnear, K.E. (ed.) Advances in Genetic Programming, pp. 265–284. MIT Press, Cambridge (1994)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Langdon, W.B.: Size fair and homologous tree genetic programming crossovers. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) GECCO 1999: Proceedings of the Genetic and Evolutionary Computation Conference (1999)
Nordin, P., Francone, F.D., Banzhaf, W.: Explicitly defined introns and destructive crossover in genetic programming. In: Rosca, J.P. (ed.) Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications, pp. 6–22 (1995)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Sandor, G.N., Erdman, A.G.: Advanced mechanism design: Analysis and synthesis, vol. 2. Prentice Hall, Englewood Cliffs (1984)
Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, pp. 215–223 (1996)
Stryer, L.: Biochemistry. Freeman, New York (1995)
Watson, J., Hopkins, N.H., Roberts, J.W., Argetsinger-Steitz, J., Weiner, A.M.: Molecular Biology of the Gene. Benjamin-Cummings (1987)
Wilson, A.C.: The molecular basis of evolution. Scientific American 253(4), 148–157 (1985)
Zhang, B.-T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ekárt, A. (2000). Shorter Fitness Preserving Genetic Programs. In: Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M., Ronald, E. (eds) Artificial Evolution. AE 1999. Lecture Notes in Computer Science, vol 1829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10721187_5
Download citation
DOI: https://doi.org/10.1007/10721187_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67846-5
Online ISBN: 978-3-540-44908-9
eBook Packages: Springer Book Archive