Multi-Tree Genetic Programming with New Operators for Transfer Learning in Symbolic Regression with Incomplete Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Al-Helali:ieeeTEC,
-
author = "Baligh Al-Helali and Qi Chen and Bing Xue and
Mengjie Zhang",
-
title = "Multi-Tree Genetic Programming with New Operators for
Transfer Learning in Symbolic Regression with
Incomplete Data",
-
journal = "IEEE Transactions on Evolutionary Computation",
-
year = "2021",
-
volume = "25",
-
number = "6",
-
pages = "1049--1063",
-
month = dec,
-
keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Incomplete Data, Transfer Learning,
Evolutionary Learning",
-
ISSN = "1089-778X",
-
DOI = "doi:10.1109/TEVC.2021.3079843",
-
size = "15 pages",
-
abstract = "Lack of knowledge is a common consequence of data
incompleteness when learning from real-world data. To
deal with such a situation, this work uses transfer
learning to re-use knowledge from different (yet
related) but complete domains. Due to its powerful
feature construction ability, genetic programming is
used to construct feature-based transformations that
map the feature space of the source domain to that of
the target domain such that their differences are
reduced. Particularly, this work proposes a new
multi-tree genetic programming-based feature
construction approach to transfer learning in symbolic
regression with missing values. It transfers knowledge
related to the importance of the features and instances
in the source domain to the target domain to improve
the learning performance. Moreover, new genetic
operators are developed to encourage minimising the
distribution discrepancy between the transformed domain
and the target domain. A new probabilistic crossover is
developed to make the well-constructed trees in the
individuals more likely to be mated than the other
trees. A new mutation operator is designed to give more
probability for the poorly-constructed trees to be
mutated. The experimental results show that the
proposed method not only achieves better performance
compared with different traditional learning methods
but also advances two recent transfer learning methods
on real-world data sets with various incompleteness and
learning scenarios.",
-
notes = "also known as \cite{9429709}",
- }
Genetic Programming entries for
Baligh Al-Helali
Qi Chen
Bing Xue
Mengjie Zhang
Citations