Human-inspired Scaling in Learning Classifier Systems: Case Study on the n-bit Multiplexer Problem Set

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Alvarez:2016:GECCO,
  author =       "Isidro M. Alvarez and Will N. Browne and 
                 Mengjie Zhang",
  title =        "Human-inspired Scaling in Learning Classifier Systems:
                 Case Study on the n-bit Multiplexer Problem Set",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "429--436",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908813",
  abstract =     "Learning classifier systems (LCSs) originated from
                 artificial cognitive systems research, but migrated
                 such that LCS became powerful classification
                 techniques. Modern LCSs can be used to extract building
                 blocks of knowledge in order to solve more difficult
                 problems in the same or a related domain. The past work
                 showed that the reuse of knowledge through the adoption
                 of code fragments, GP-like sub-trees, into the XCS
                 learning classifier system framework could provide
                 advances in scaling. However, unless the pattern
                 underlying the complete domain can be described by the
                 selected LCS representation of the problem, a limit of
                 scaling will eventually be reached. This is due to LCSs
                 divide and conquer approach rule-based solutions, which
                 entails an increasing number of rules (subclauses) to
                 describe a problem as it scales. Inspired by human
                 problem solving abilities, the novel work in this paper
                 seeks to reuse learned knowledge and learned
                 functionality to scale to complex problems by
                 transferring them from simpler problems. Progress is
                 demonstrated on the benchmark Multiplexer (Mux) domain,
                 albeit the developed approach is applicable to other
                 scalable domains. The fundamental axioms necessary for
                 learning are proposed. The methods for transfer
                 learning in LCSs are developed. Also, learning is
                 recast as a decomposition into a series of
                 sub-problems. Results show that from a conventional
                 tabula rasa, with only a vague notion of what
                 subordinate problems might be relevant, it is possible
                 to learn a general solution to any n-bit Mux problem
                 for the first time. This is verified by tests on the
                 264, 521 and 1034 bit Mux problems.",
  notes =        "GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Isidro M Alvarez Will N Browne Mengjie Zhang

Citations