Performance Analysis of Self-Supervised Strategies for Standard Genetic Programming

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

@InProceedings{rodrigues:2023:GECCOcomp,

• author = "Nuno Rodrigues and Jose Almeida and Sara Silva",
• title = "Performance Analysis of {Self-Supervised} Strategies for Standard Genetic Programming",
• 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 = "627--630",
• 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, tabular data, self-supervised learning: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590748",
• size = "4 pages",
• abstract = "Self-supervised learning (SSL) methods have been widely used to train deep learning models for computer vision and natural language processing domains. They leverage large amounts of unlabeled data to help pretrain models by learning patterns implicit in the data. Recently, new SSL techniques for tabular data have been developed, using new pretext tasks that typically aim to reconstruct a corrupted input sample and yielding models which are, ideally, robust feature transforms. In this paper, we pose the research question of whether genetic programming is capable of leveraging data processed using SSL methods to improve its performance. We test this hypothesis by assuming different amounts of labeled data on seven different datasets (five OpenML benchmarking datasets and two real-world datasets). The obtained results show that in almost all problems, standard genetic programming is not able to capitalize on the learned representations, producing results equal to or worse than using the labeled partitions.",
• 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 Nuno Miguel Rodrigues Domingos Jose Almeida Sara Silva

Citations