An Analysis of Choice Functions for Fuzzy ART Using Grammatical Evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{gerber:2023:GECCOcomp2,
-
author = "Mia Gerber and Nelishia Pillay",
-
title = "An Analysis of Choice Functions for Fuzzy {ART} Using
Grammatical Evolution",
-
booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
-
year = "2023",
-
editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
-
pages = "571--574",
-
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, grammatical
evolution, fuzzy art, automated design: Poster",
-
isbn13 = "9798400701191",
-
DOI = "doi:10.1145/3583133.3590554",
-
size = "4 pages",
-
abstract = "The Fuzzy Adaptive Resonance Theory (ART) algorithm is
effective for unsupervised clustering. The Fuzzy ART
choice function is an integral part of the Fuzzy ART
algorithm. One of the challenges is that different
choice functions are effective for different datasets.
This work evolves the choice function using GE. The
study compares the evolved choice functions to manually
created choice functions. This study compares two
different grammars for the GE, a basic grammar that
includes only functions from the Fuzzy ART algorithm
and an extended grammar that includes additional
functions. This work also compares different fitness
functions for GE. Analysis is done using ten UCI
benchmark datasets and three real-world sentiment
analysis datasets, it is found that the evolved
functions using the extended grammar perform better
than the manually created functions. The best fitness
function to use for the GE is dataset dependent.",
-
notes = "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
Mia Gerber
Nelishia Pillay
Citations