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.
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer [email protected] |
| Pages | 213-220 |
| Number of pages | 8 |
| ISBN (Print) | 3540628584 |
| State | Published - Jan 1 1997 |
| Externally published | Yes |