Using Localised ‘Gossip’ to Structure Distributed Learning
نویسنده
چکیده
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 coevolves 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 outperforms a global approach, but the space of attributes within which this evolutionary process occurs can greatly effect the efficiency of the technique.
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