An Analysis of Choice Functions for Fuzzy ART Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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