A comparison of semantic-based initialization methods for genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Ahmad:2018:GECCOcomp,
  author =       "Hammad Ahmad and Thomas Helmuth",
  title =        "A comparison of semantic-based initialization methods
                 for genetic programming",
  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 =        "1878--1881",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205651.3208218",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "During the initialization step, a genetic programming
                 (GP) system traditionally creates a population of
                 completely random programs to populate the initial
                 population. These programs almost always perform poorly
                 in terms of their total error---some might not even
                 output the correct data type. In this paper, we present
                 new methods for initialization that attempt to generate
                 programs that are somewhat relevant to the problem
                 being solved and/or increase the initial diversity
                 (both error and behavioural diversity) of the
                 population prior to the GP run. By seeding the
                 population---and thereby eliminating worthless programs
                 and increasing the initial diversity of the
                 population---we hope to improve the performance of the
                 GP system. Here, we present two novel techniques for
                 initialization (Lexicase Seeding and Pareto Seeding)
                 and compare them to a previous method (Enforced Diverse
                 Populations) and traditional, non-seeded
                 initialization. Surprisingly, we found that none of the
                 initialization m",
  notes =        "Also known as \cite{3208218} 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 Hammad Ahmad Thomas Helmuth

Citations