Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space (1997)
| Venue: | Evolutionary Computation |
| Citations: | 53 - 12 self |
BibTeX
@ARTICLE{Salustowicz97probabilisticincremental,
author = {Rafal Salustowicz and Jürgen Schmidhuber},
title = {Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space},
journal = {Evolutionary Computation},
year = {1997},
volume = {5},
pages = {123--141}
}
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Abstract
. Probabilistic Incremental Program Evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions [Schmidhuber, 1997], PopulationBased 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. 1 Introduction Probabilistic Incremental Program Evolution (PIPE) synthesizes programs which compute solutions to a given problem. PIPE is inspired by recent work on learning with probabilistic programming languages [Schmidhuber, 1997] and by Population-Based Incremental Learning (PBIL) [Baluja and Caruana, 1995]. PIPE evolves tree-coded programs such a...
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