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:1.8129

@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