Genetic Programming-Based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{zhang:2023:GECCOcompA,
-
author = "Hengzhe Zhang and Aimin Zhou and Qi Chen and
Bing Xue and Mengjie Zhang",
-
title = "Genetic {Programming-Based} Evolutionary Feature
Construction for Heterogeneous Ensemble Learning [Hot
of the Press]",
-
booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
-
year = "2023",
-
editor = "Alberto Moraglio",
-
pages = "49--50",
-
address = "Lisbon, Portugal",
-
series = "GECCO '23",
-
month = "15-19 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, evolutionary
feature construction, heterogeneous ensemble learning",
-
isbn13 = "9798400701191",
-
DOI = "doi:10.1145/3583133.3595831",
-
size = "2 pages",
-
abstract = "This Hof-off-the-Press paper summarizes our recently
published work, {"}SR-Forest: A Genetic Programming
based Heterogeneous Ensemble Learning Method,{"}
published in IEEE Transactions on Evolutionary
Computation [4]. This paper presents SR-Forest, a novel
genetic programming-based heterogeneous ensemble
learning method, which combines the strengths of
decision trees and genetic programming-based symbolic
regression methods. Rather than treating genetic
programming-based symbolic regression methods as
competitors to random forests, we propose to enhance
the performance of random forests by incorporating
genetic programming as a complementary technique. We
introduce a guided mutation operator, a multi-fidelity
evaluation strategy, and an ensemble selection
mechanism to accelerate the search process, reduce
computational costs, and improve predictive
performance. Experimental results on a regression
benchmark with 120 datasets show that SR-Forest
outperforms 25 existing symbolic regression and
ensemble learning methods. Moreover, we demonstrate the
effectiveness of SR-Forest on an XGBoost hyperparameter
performance prediction task, which is an important
application area of ensemble learning methods. Overall,
SR-Forest provides a promising approach to solving
regression problems and can serve as a valuable tool in
real-world applications.",
-
notes = "Also known as \cite{zhang:2023:GECCOcomp}. GECCO-2023
A Recombination of the 32nd International Conference on
Genetic Algorithms (ICGA) and the 28th Annual Genetic
Programming Conference (GP)",
- }
Genetic Programming entries for
Hengzhe Zhang
Aimin Zhou
Qi Chen
Bing Xue
Mengjie Zhang
Citations