Symbolic regression and feature construction with GP-GOMEA applied to radiotherapy dose reconstruction of childhood cancer survivors

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@InProceedings{Virgolin:2018:GECCO,
  author =       "Marco Virgolin and Tanja Alderliesten and 
                 Arjan Bel and Cees Witteveen and Peter A. N. Bosman",
  title =        "Symbolic regression and feature construction with
                 {GP-GOMEA} applied to radiotherapy dose reconstruction
                 of childhood cancer survivors",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  editor =       "Hernan Aguirre and Keiki Takadama and 
                 Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and 
                 Andrew M. Sutton and Satoshi Ono and Francisco Chicano and 
                 Shinichi Shirakawa and Zdenek Vasicek and 
                 Roderich Gross and Andries Engelbrecht and Emma Hart and 
                 Sebastian Risi and Ekart Aniko and Julian Togelius and 
                 Sebastien Verel and Christian Blum and Will Browne and 
                 Yusuke Nojima and Tea Tusar and Qingfu Zhang and 
                 Nikolaus Hansen and Jose Antonio Lozano and 
                 Dirk Thierens and Tian-Li Yu and Juergen Branke and 
                 Yaochu Jin and Sara Silva and Hitoshi Iba and 
                 Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and 
                 Federica Sarro and Giuliano Antoniol and Anne Auger and 
                 Per Kristian Lehre",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "1395--1402",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205604",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "the recently introduced Gene-pool Optimal Mixing
                 Evolutionary Algorithm for Genetic Programming
                 (GP-GOMEA) has been shown to find much smaller
                 solutions of equally high quality compared to other
                 state-of-the-art GP approaches. This is an interesting
                 aspect as small solutions better enable human
                 interpretation. In this paper, an adaptation of
                 GP-GOMEA to tackle real-world symbolic regression is
                 proposed, in order to find small yet accurate
                 mathematical expressions, and with an application to a
                 problem of clinical interest. For radiotherapy dose
                 reconstruction, a model is sought that captures
                 anatomical patient similarity. This problem is
                 particularly interesting because while features are
                 patient-specific, the variable to regress is a
                 distance, and is defined over patient pairs. We show
                 that on benchmark problems as well as on the
                 application, GP-GOMEA outperforms variants of standard
                 GP. To find even more accurate models, we further
                 consider an evolutionary meta learning approach, where
                 GP-GOMEA is used to construct small, yet effective
                 features for a different machine learning algorithm.
                 Experimental results show how this approach
                 significantly improves the performance of linear
                 regression, support vector machines, and random forest,
                 while providing meaningful and interpretable
                 features.",
  notes =        "Also known as \cite{3205604} GECCO-2018 A
                 Recombination of the 27th International Conference on
                 Genetic Algorithms (ICGA-2018) and the 23rd Annual
                 Genetic Programming Conference (GP-2018)",
}

Genetic Programming entries for Marco Virgolin Tanja Alderliesten Arjan Bel Cees Witteveen Peter A N Bosman

Citations