title: Using Localised ‘Gossip’ to Structure Distributed Learning
creator: Edmonds, Bruce
subject: Machine Learning
subject: Ecology
subject: Social simulation
description: 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 overlappingt localities in a space and solutions are then evolved in those localites.  Good solutions are not only crossed with others to search for better solutions but also they propogate 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 occcurence 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.
publisher: AISB
date: 2005
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4265/1/ulgtsdl.pdf
format: text/html
identifier: http://cogprints.org/4265/2/ulgtsdl.html
identifier:   Edmonds, Bruce  (2005) Using Localised ‘Gossip’ to Structure Distributed Learning.  [Conference Paper]     
relation: http://cogprints.org/4265/