A multidimensional genetic programming approach for identifying epsistatic gene interactions

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

@InProceedings{LaCava:2018:GECCOcomp,
  author =       "William {La Cava} and Sara Silva and Kourosh Danai and 
                 Lee Spector and Leonardo Vanneschi and Jason H. Moore",
  title =        "A multidimensional genetic programming approach for
                 identifying epsistatic gene interactions",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  year =         "2018",
  editor =       "Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and 
                 Shigeru Obayashi and Bogdan Filipic and 
                 Thomas Bartz-Beielstein and Grant Dick and 
                 Masaharu Munetomo and Silvino {Fernandez Alzueta} and Thomas Stuetzle and 
                 Pablo Valledor Pellicer and Manuel Lopez-Ibanez and 
                 Daniel R. Tauritz and Pietro S. Oliveto and 
                 Thomas Weise and Borys Wrobel and Ales Zamuda and 
                 Anne Auger and Julien Bect and Dimo Brockhoff and 
                 Nikolaus Hansen and Rodolphe {Le Riche} and Victor Picheny and 
                 Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and 
                 Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and 
                 Richard Duro and Joshua Auerbach and 
                 Harold {de Vladar} and Antonio J. Fernandez-Leiva and JJ Merelo and 
                 Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and 
                 Francisco {Chavez de la O} and Ozgur Akman and 
                 Khulood Alyahya and Juergen Branke and Kevin Doherty and 
                 Jonathan Fieldsend and Giuseppe Carlo Marano and 
                 Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and 
                 Stefan Wagner and Michael Affenzeller and 
                 Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and 
                 Riyad Alshammari and Tokunbo Makanju and 
                 Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and 
                 John R. Woodward and Shin Yoo and John McCall and 
                 Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and 
                 Masaya Nakata and Anthony Stein and 
                 Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and 
                 Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and William {La Cava} and 
                 Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and 
                 Ivanoe {De Falco} and Antonio {Della Cioppa} and 
                 Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and 
                 Giovanni Iacca and Ahmed Hallawa and Anil Yaman and 
                 Alma Rahat and Handing Wang and Yaochu Jin and 
                 David Walker and Richard Everson and Akira Oyama and 
                 Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and 
                 Pramudita Satria Palar",
  isbn13 =       "978-1-4503-5764-7",
  pages =        "23--24",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208217",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We propose a novel methodology for binary and
                 multiclass classification that uses genetic programming
                 to construct features for a nearest centroid
                 classifier. The method, coined M4GP, improves upon
                 earlier approaches in this vein (M2GP and M3GP) by
                 simplifying the program encoding, using advanced
                 selection methods, and archiving solutions during the
                 run. In our recent paper, we test this strategy against
                 traditional GP formulations of the classification
                 problem, showing that this framework outperforms
                 boolean and floating point encodings. In comparison to
                 several machine learning techniques, M4GP achieves the
                 best overall ranking on benchmark problems. We then
                 compare our algorithm against state-of-the-art machine
                 learning approaches to the task of disease
                 classification using simulated genetics datasets with
                 up to 5000 features. The results suggest that our
                 proposed approach performs on par with the best results
                 in literature with less computation time, while
                 producing simpler models.",
  notes =        "Also known as \cite{3208217} 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 William La Cava Sara Silva Kourosh Danai Lee Spector Leonardo Vanneschi Jason H Moore

Citations