- author = "Bruce Edmonds",
- title = "Using Localised 'Gossip' to Structure Distributed Learning",
- 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 = "
http://bruce.edmonds.name/ulgtsdl/ulgtsdl.pdf",
-
URL = "
http://cfpm.org/cpmrep142.html",
- 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 efficiency.",
- 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.",
