Semantic Linear Genetic Programming for Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{9810862,
-
author = "Zhixing Huang and Yi Mei and Jinghui Zhong",
-
title = "Semantic Linear Genetic Programming for Symbolic
Regression",
-
journal = "IEEE Transactions on Cybernetics",
-
note = "Early Access",
-
keywords = "genetic algorithms, genetic programming",
-
DOI = "doi:10.1109/TCYB.2022.3181461",
-
abstract = "Symbolic regression (SR) is an important problem with
many applications, such as automatic programming tasks
and data mining. Genetic programming (GP) is a commonly
used technique for SR. In the past decade, a branch of
GP that uses the program behaviour to guide the search,
called semantic GP (SGP), has achieved great success in
solving SR problems. However, existing SGP methods only
focus on the tree-based chromosome representation and
usually encounter the bloat issue and unsatisfactory
generalisation ability. To address these issues, we
propose a new semantic linear GP (SLGP) algorithm. In
SLGP, we design a new chromosome representation to
encode the programs and semantic information in a
linear fashion. To use the semantic information more
effectively, we further propose a novel semantic
genetic operator, namely, mutate-and-divide
propagation, to recursively propagate the semantic
error within the linear program. The empirical results
show that the proposed method has better training and
test errors than the state-of-the-art algorithms in
solving SR problems and can achieve a much smaller
program size.",
- }
Genetic Programming entries for
Zhixing Huang
Yi Mei
Jinghui Zhong
Citations