Schema-based diversification in genetic programming

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

@InProceedings{Burlacu:2018:GECCO,
  author =       "Bogdan Burlacu and Michael Affenzeller",
  title =        "Schema-based diversification in genetic programming",
  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 =        "1111--1118",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205594",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "In genetic programming (GP), population diversity
                 represents a key aspect of evolutionary search and a
                 major factor in algorithm performance. In this paper we
                 propose a new schema-based approach for observing and
                 steering the loss of diversity in GP populations. We
                 employ a well-known hyperschema definition from the
                 literature to generate tree structural templates from
                 the population's genealogy, and use them to guide the
                 search via localized mutation within groups of
                 individuals matching the same schema. The approach
                 depends only on genealogy information and is easily
                 integrated with existing GP variants. We demonstrate
                 its potential in combination with Offspring Selection
                 GP (OSGP) on a series of symbolic regression benchmark
                 problems where our algorithmic variant called OSGP-S
                 obtains superior results.",
  notes =        "Also known as \cite{3205594} 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 Bogdan Burlacu Michael Affenzeller

Citations