Integration of Code-Fragment based Learning Classifier Systems for Multiple Domain Perception and Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Liu:2016:CEC,
  author =       "Yi Liu and Muhammad Iqbal and Isidro Alvarez and 
                 Will N. Browne",
  title =        "Integration of Code-Fragment based Learning Classifier
                 Systems for Multiple Domain Perception and Learning",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "2177--2184",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744057",
  abstract =     "It has been shown that identifying building blocks of
                 knowledge and then reusing them to solve complex
                 problems is a practical and useful endeavour. Previous
                 work made it possible to solve various, until then,
                 intractable tasks. However, the individual algorithms
                 targeted one specific problem type, e.g. scalable
                 problems or domains with repeating patterns. The
                 question that arises is: Can the disparate techniques
                 be combined into a single approach to solve more
                 complex problems that span several domains or that may
                 be unknown to the agent? The first stage in developing
                 such a system is to be able to recognise domains from
                 unidentified input stimuli and identify the approaches
                 best suited to them. The novel work here aims to
                 realise this primary stage by combining several
                 code-fragment (CF) based XCS systems. The stimulus and
                 its guiding effect, will be instrumental in helping the
                 agent decide which of its stored systems is the most
                 capable of solving the problem, or if there is a
                 conflict between possible solutions. Importantly, the
                 agent will be capable of determining if the current
                 problem is entirely new, in which case it spawns a
                 training agent to produce a tractable solution to store
                 and reuse. The proposed technique relies on the proven
                 benefits in scalability of CF based systems and
                 furthers the body of knowledge by tackling unknown
                 problems (to the agent). The main contribution of this
                 research is that a system of proven CF techniques is
                 used for the first time. We show that by using the new
                 CF system, it is possible to identify an unknown
                 problem and to arrive at a viable solution.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Yi Liu Muhammad Iqbal Isidro Alvarez Will N Browne

Citations