Probabilistic Incremental Program Evolution: Stochastic search through program space
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- 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 (Lecture Notes in Artificial Intelligence), vol 1224. Springer, Berlin, Heidelberg
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.
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