Abstract
Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting tool for automatic programming and machine learning. One weakness is the enormous time required for evolving complex programs. In this paper we present a method for accelerating evolution speed of genetic programming by active selection of fitness cases during the run. In contrast to conventional genetic programming in which all the given training data are used repeatedly, the presented method evolves programs using only a subset of given data chosen incrementally at each generation. This method is applied to the evolution of collective behaviors for multiple robotic agents. Experimental evidence supports that evolving programs on an incrementally selected subset of fitness cases can significantly reduce the fitness evaluation time without sacrificing generalization accuracy of the evolved programs.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gathercole, C. and Ross, P. 1994. Dynamic training subset selection for supervised learning in genetic programming. In Y. Davidor, H.-P. Schwefel, and R. Männer, (eds.). Parallel Problem Solving from Nature III, Berlin: Springer-Verlag, Pages 312–321.
Gathercole, C. and Ross, P. 1997. Small populations over many generations can beat large populations over few generations in genetic programming. In J.R. Koza (eds.). Genetic Programming 1997. Cambridge, MA: The MIT Press. Pages 111–118.
Haynes, T., Sen, S., Schoenefeld, D., and Wainwright, R. 1995. Evolving a team, In Proc. AAAI-95 Fall Symposium on Genetic Programming AAAI Press. Pages 23–30.
Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press.
Luke, S. and Spector, L. 1996. Evolving teamwork and coordination with genetic programming. In J.R. Koza (eds.). Proc. First Genetic Programming Conf. Cambridge, MA: The MIT Press. Pages 150–156.
Soule, T., Foster, J. A., and Dickinson, J. 1996. Code growth in genetic programming. In J.R. Koza (eds.). Genetic Programming 1996. Cambridge, MA: The MIT Press. Pages 215–223.
Zhang, B. T. 1992. Learning by Genetic Neural Evolution, DISKI Vol. 16, 268 pages, ISBN 3-929037-16-6, Infix-Verlag, St. Augustin/Bonn.
Zhang, B. T. 1994. Accelerated learning by active example selection, International Journal of Neural Systems, 5(1): 67–75.
Zhang, B. T. and Cho, D. Y. 1998. Fitness switching: Evolving complex group behaviors using genetic programming. In Genetic Programming 1998, Madison, Wisconsin, pp. 431–438, 1998.
Zhang, B. T. Mühlenbein, H. 1995. Balancing accuracy and parsimony in genetic programming. Evolutionary Computation. 3(1) 17–38.
Zhang, B. T. and Veenker, G. 1991. Focused incremental learning for improved generalization with reduced training sets, Proc. Int. Conf. Artificial Neural Networks, Kohonen, T. et al. (eds.) North-Holland, pp. 227–232.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, BT., Cho, DY. (1999). Genetic Programming with Active Data Selection. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_20
Download citation
DOI: https://doi.org/10.1007/3-540-48873-1_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65907-5
Online ISBN: 978-3-540-48873-6
eBook Packages: Springer Book Archive