Symbolic-regression boosting

author = "Moshe Sipper and Jason H. Moore",

title = "Symbolic-regression boosting",

journal = "Genetic Programming and Evolvable Machines",

year = "2021",

volume = "22",

number = "3",

pages = "357--381",

month = sep,

keywords = "genetic algorithms, genetic programming, Symbolic regression, Gradient boosting",

ISSN = "1389-2576",

URL = "

https://rdcu.be/cFCTA",

DOI = "

doi:10.1007/s10710-021-09400-0",

size = "25 pages",

abstract = "Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: symbolic-regression boosting. Experiments over 98 regression datasets show that by adding a small number of boosting stages, between 2 and 5, to a symbolic regressor, statistically significant improvements can often be attained. We note that coding SyRBo on top of any symbolic regressor is straightforward, and the added cost is simply a few more evolutionary rounds. SyRBo is essentially a simple add-on that can be readily added to an extant symbolic regressor, often with beneficial results.",