Surrogate-Assisted Genetic Programming in Efficient Neural Architecture Search for Image Segmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.8512

@InProceedings{fuentes-tomas:2025:GECCOcomp,

• author = "Jose-Antonio Fuentes-Tomas and Efren Mezura-Montes and Hector-Gabriel Acosta-Mesa and David Herrera-Sanchez",
• title = "Surrogate-Assisted Genetic Programming in Efficient Neural Architecture Search for Image Segmentation",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Bing Xue and Dennis Wilson",
• pages = "715--718",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, neuroevolution, surrogate model: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726723",
• DOI = "doi:10.1145/3712255.3726723",
• size = "4 pages",
• abstract = "Recently, Convolutional Neural Networks (CNNs) have performed well in pattern recognition tasks such as image segmentation. However, designing a CNN for a particular dataset requires experience in choosing from feature extraction and aggregation operators. Neural Architecture Search (NAS) is automating the design of neural networks for a specific task. By leveraging its syntax tree representation, genetic programming (GP) can be adapted as a search strategy in the NAS (ENAS) problem. Solutions evaluation implies the models' weight optimization, making ENAS computationally expensive. Nonetheless, surrogate models can reduce the costly evaluations by predicting fitness solutions. Generating a surrogate model from variable-size tree-based GP solutions usually requires a tree-to-structured data conversion to use standard baseline regression models. This paper compares several surrogate models used to assist GP-based algorithms. Also, phenotype-level features are proposed to be included in converting syntax-tree solutions to sequence-vectors. The surrogate models are applied to optimize U-Net-type networks, showing promising results in reducing the number of costly evaluations and the execution time without affecting the segmentation performance.",
• notes = "GECCO-2025 NE A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Jose-Antonio Fuentes-Tomas Efren Mezura-Montes Hector-Gabriel Acosta-Mesa David Herrera-Sanchez

Citations