Grammar evolution and symbolic regression for astrometric centering of Hubble Space Telescope images

Created by W.Langdon from gp-bibliography.bib Revision:1.7644

@InProceedings{Sarmiento:2024:GI,

• author = "Ricardo Sarmiento and Marina {de la Cruz} and Alfonso Ortega and Roberto Baena-Galle and Terrence Girard and Dana Casetti-Dinescu and Alejandro Cervantes",
• title = "Grammar evolution and symbolic regression for astrometric centering of {Hubble} Space Telescope images",
• booktitle = "13th International Workshop on Genetic Improvement @ICSE 2024",
• year = "2024",
• editor = "Gabin An and Aymeric Blot and Vesna Nowack and Oliver Krauss and and and Justyna Petke",
• address = "Lisbon",
• month = "16 " # apr,
• publisher = "ACM",
• keywords = "genetic algorithms, genetic programming, Genetic Improvement, Grammatical evolution, symbolic regression, astrometry, Wide Field Planetary Camera 2, WFPC2, Hubble Space Telescope, HST, point spread functions, PSF",
• isbn13 = "979-8-4007-0573-1/24/04",
• URL = "http://gpbib.cs.ucl.ac.uk/gi2024/ICSE_24_GIGE_astronomy_cr_vldtd.pdf",
• DOI = "doi:10.1145/3643692.3648264",
• slides_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/GEastronomy_GIICSE24.pdf",
• video_url = "http://gpbib.cs.ucl.ac.uk/gi2024/gi_2024_slides/gi_icse24_aop.mp4",
• size = "8 pages",
• abstract = "Symbolic regression, in general, and genetic models, in particular, are promising approaches to mathematical modeling in astrometry where it is not always clear which is the fittest analytic expression depending on the problem under consideration. Several attempts and increasing research efforts are being made in this direction mainly from the Genetic Programming (GP) viewpoint. Our proposal is, as far as we know, the first one to apply Grammatical Evolution (GE) in this domain. GE (and further GE extensions) aim to outperform GP limitations by incorporating formal language stools to guarantee the correctness (both syntactic and semantic) of the generated expressions. The current contribution is a first proof to check the viability of GE on astrometric real datasets. Its success in finding adequate parameters for predefined families of functions in star centering (Gaussian and Moffat PSFs) with simple and naive GE experiments supports our hypothesis on taking advantage of the expressive power of GE to tackle astrometry scenarios of interest and hence greatly improve current astrometric software thanks to specific genetic approaches.",
• notes = "GI @ ICSE 2024, part of \cite{an:2024:GI}",

}

Genetic Programming entries for Ricardo Sarmiento Marina de la Cruz Echeandia Alfonso Ortega de la Puente Roberto Baena-Galle Terrence Girard Dana Casetti-Dinescu Alejandro Cervantes

Citations