Neuro-guided genetic programming: prioritizing evolutionary search with neural networks

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

@InProceedings{Liskowski:2018:GECCOa,
  author =       "Pawel Liskowski and Iwo Bladek and Krzysztof Krawiec",
  title =        "Neuro-guided genetic programming: prioritizing
                 evolutionary search with neural networks",
  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 =        "1143--1150",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205629",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "When search operators in genetic programming (GP)
                 insert new instructions into programs, they usually
                 draw them uniformly from the available instruction set.
                 Preferring some instructions to others would require
                 additional domain knowledge, which is typically
                 unavailable. However, it has been recently demonstrated
                 that the likelihoods of instructions occurrence in a
                 program can be reasonably well estimated from its
                 input-output behaviour using a neural network. We
                 exploit this idea to bias the choice of instructions
                 used by search operators in GP. Given a large sample of
                 programs and their input-output behaviours, a neural
                 network is trained to predict the presence of
                 individual instructions. When applied to a new program
                 synthesis task, the network is first queried on the set
                 of examples that define the task, and the obtained
                 probabilities determine the frequencies of using
                 instructions in initialization and mutation operators.
                 This priming leads to significant improvements of the
                 odds of successful synthesis on a range of
                 benchmarks.",
  notes =        "Also known as \cite{3205629} 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 Pawel Liskowski Iwo Bladek Krzysztof Krawiec

Citations