Efficient Distributed Genetic Algorithm for Rule extraction

This paper presents an Efficient Distributed Genetic Algorithm for classification Rule extraction in data mining (EDGAR), which promotes a new method of data distribution in computer networks. This is done by spatial partitioning of the population into several semi-isolated nodes, each evolving in parallel and possibly exploring different regions of the search space. The presented algorithm shows some advantages when compared with other distributed algorithms proposed in the specific literature. In this way, some results are presented showing significant learning rate speedup without compromising the accuracy.