Program synthesis tasks usually specify only the desired output of a program and do not state any expectations about its internal behavior. The intermediate execution states reached by a running program can be nonetheless deemed as more or less preferred accordingto their information content with respect to the desired output. In this paper, a consistency measure is proposed that implements this observation. When used as an additional search objective in a typical genetic programming setting, this measure improves the success rate on a suite of 35 benchmarks in a statistically significant way.
@INPROCEEDINGS { LNCS86720434,
ABSTRACT = { Program synthesis tasks usually specify only the desired output of a program and do not state any expectations about its internal behavior. The intermediate execution states reached by a running program can be nonetheless deemed as more or less preferred accordingto their information content with respect to the desired output. In this paper, a consistency measure is proposed that implements this observation. When used as an additional search objective in a typical genetic programming setting, this measure improves the success rate on a suite of 35 benchmarks in a statistically significant way. },
AUTHOR = { Krzysztof Krawiec and Armando Solar-Lezama },
BOOKTITLE = { Parallel Problem Solving from Nature -- PPSN XIII },
DOI = { 10.1007/978-3-319-10762-2_43 },
EDITOR = { Thomas Bartz-Beielstein and J\"{u}rgen Branke and Bogdan Filipi\v{c} and Jim Smith },
ISBN = { 978-3-319-10761-5 },
LOCATION = { Heidelberg },
PAGES = { 434--443 },
PUBLISHER = { Springer },
SERIES = { Lecture Notes in Computer Science },
TITLE = { Improving Genetic Programming with Behavioral Consistency Measure },
VOLUME = { 8672 },
YEAR = { 2014 },
1 = { https://doi.org/10.1007/978-3-319-10762-2_43 },
}