Distributed, collaborative data analysis from heterogeneous sites using a scalable evolutionary technique

This paper documents an early effort to develop an experimental, collaborat ive data analysis technique for learning classifiers from a collection of h eterogeneous datasets distributed over a network. The proposed technique ma kes use of a scalable evolutionary algorithm, called the GEMGA to classify datasets. This paper describes the developed technique and the results of t he use of this technique through the application of this system for several domains, including distributed fault detection in an electrical power dist ribution network.