Using Localised 'Gossip' to Structure Distributed Learning

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

  author =       "Bruce Edmonds",
  title =        "Using Localised 'Gossip' to Structure Distributed
  institution =  "Centre for Policy Modelling, Manchester Metropolitan
                 University Business School",
  year =         "2005",
  type =         "CPM Report",
  number =       "CPM-04-142",
  address =      "UK",
  month =        "15th " # may,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  abstract =     "The idea of a {"}memetic{"} spread of solutions
                 through a human culture in parallel to their
                 development is applied as a distributed approach to
                 learning. Local parts of a problem are associated with
                 a set of overlapping localities in a space and
                 solutions are then evolved in those localities. Good
                 solutions are not only crossed with others to search
                 for better solutions but also they propagate across the
                 areas of the problem space where they are relatively
                 successful. Thus the whole population co-evolves
                 solutions with the domains in which they are found to
                 work. This approach is compared to the equivalent
                 global evolutionary computation approach with respect
                 to predicting the occurrence of heart disease in the
                 Cleveland data set. It greatly outperforms the global
                 approach, but the space of attributes within which this
                 evolutionary process occurs can effect its
  notes =        "Presented at the {"}Engineering with Social
                 Metaphors{"} day of the AISB Symposium on Socially
                 Inspired Computing, University of Hertfordship, April
                 2005. \cite{edmonds:2005:esm}",
  size =         "12 pages",
  notes =        "

                 'geographic separation' in space of inputs. How this is
                 done has dramatic effect on effectiveness of this
                 approach. 'exact distance metric did not noticeable
                 effect the results'.

                 Global GP only using 10 percent of training data.",

Genetic Programming entries for Bruce Edmonds