Abstract
Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions [Schmidhuber, 1997], Population-Based Incremental Learning (PBIL) [Baluja and Caruana, 1995] and tree-coding of programs used in variants of Genetic Programming (GP) [Cramer, 1985; Koza, 1992]. PIPE uses a stochastic selection method for successively generating better and better programs according to an adaptive “probabilistic prototype tree”. No crossover operator is used. We compare PIPE to Koza's GP variant on a function regression problem and the 6-bit parity problem.
Chapter PDF
References
Baluja, S. and Caruana, R. (1995). Removing the genetics from the standard genetic algorithm. In Prieditis, A. and Russell, S., editors, Machine Learning: Proceedings of the Twelfth International Conference, pages 38–46. Morgan Kaufmann Publishers, San Francisco, CA.
Cramer, N. L. (1985). A representation for the adaptive generation of simple sequential programs. In Grefenstette, J., editor, Proceedings of an International Conference on Genetic Algorithms and Their Applications, Hillsdale NJ. Lawrence Erlbaum Associates.
Dickmanns, D., Schmidhuber, J., and Winklhofer, A. (1987). Der genetische Algorithmus: Eine Implementierung in Prolog. Fortgeschrittenenpraktikum, Institut für Informatik, Lehrstuhl Prof. Radig, Technische Universität München.
Koza, J. R. (1992). Genetic Programming — On the Programming of Computers by Means of Natural Selection. MIT Press.
Schmidhuber, J. (1997). A general method for incremental self-improvement and multi-agent learning in unrestricted environments. In Yao, X., editor, Evolutionary Computation: Theory and Applications. Scientific Publ. Co., Singapore. In press.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sałustowicz, R., Schmidhuber, J. (1997). Probabilistic Incremental Program Evolution: Stochastic search through program space. In: van Someren, M., Widmer, G. (eds) Machine Learning: ECML-97. ECML 1997. Lecture Notes in Computer Science, vol 1224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62858-4_86
Download citation
DOI: https://doi.org/10.1007/3-540-62858-4_86
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-62858-3
Online ISBN: 978-3-540-68708-5
eBook Packages: Springer Book Archive