P-Tree programming

We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagat...

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Veröffentlicht in:SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017 S. 1 - 7
1. Verfasser: Oesch, Christian
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.11.2017
Schlagworte:
Benchmark testing
Open area test sites
Probability distribution
Programming
Prototypes
Sociology
Statistics
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Abstract We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extent. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression.
AbstractList We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype tree. From this prototype tree we form program instances which we evaluate on a given problem. The error values from the evaluations are propagated through the prototype tree. We use them to update the probability distributions that determine the symbol choices of further instances. The iterative method is applied to several symbolic regression benchmarks from the literature. It outperforms standard Genetic Programming to a large extent. Furthermore, it relies on a concise set of parameters which are held constant for all problems. The algorithm can be employed for most of the typical computational intelligence tasks such as classification, automatic program induction, and symbolic regression.
Author Oesch, Christian
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Snippet We propose a novel method for automatic program synthesis. P-Tree Programming represents the program search space through a single probabilistic prototype...
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SubjectTerms Benchmark testing
Open area test sites
Probability distribution
Programming
Prototypes
Sociology
Statistics
Title P-Tree programming
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