Shape-constrained multi-objective genetic programming for symbolic regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{HAIDER:2023:asoc,
-
author = "C. Haider and F. O. {de Franca} and B. Burlacu and
G. Kronberger",
-
title = "Shape-constrained multi-objective genetic programming
for symbolic regression",
-
journal = "Applied Soft Computing",
-
volume = "132",
-
pages = "109855",
-
year = "2023",
-
ISSN = "1568-4946",
-
DOI = "doi:10.1016/j.asoc.2022.109855",
-
URL = "https://www.sciencedirect.com/science/article/pii/S1568494622009048",
-
keywords = "genetic algorithms, genetic programming,
Multi-objective optimization, Shape-constrained
regression, Symbolic regression",
-
abstract = "We describe and analyze algorithms for
shape-constrained symbolic regression, which allow the
inclusion of prior knowledge about the shape of the
regression function. This is relevant in many areas of
engineering - in particular, when data-driven models,
which are based on data of measurements must exhibit
certain properties (e.g. positivity, monotonicity, or
convexity/concavity). To satisfy these properties, we
have extended multi-objective algorithms with shape
constraints. A soft-penalty approach is used to
minimize both the constraint violations and the
prediction error. We use the non-dominated sorting
genetic algorithm (NSGA-II) as well as the
multi-objective evolutionary algorithm based on
decomposition (MOEA/D). The algorithms are tested on a
set of models from physics textbooks and compared
against previous results achieved with single objective
algorithms. Further, we generated out-of-domain samples
to test the extrapolation behavior using shape
constraints and added a different level of noise on the
training data to verify if shape constraints can still
help maintain the prediction errors to a minimum and
generate valid models. The results showed that the
multi-objective algorithms were capable of finding
mostly valid models, also when using a soft-penalty
approach. Further, we investigated that NSGA-II
achieved the best overall ranks on high noise
instances",
- }
Genetic Programming entries for
Christian Haider
Fabricio Olivetti de Franca
Bogdan Burlacu
Gabriel Kronberger
Citations