Efficient Distributed Genetic Algorithm for Rule Extraction
Antonio Peregrín, Miguel Rodríguez · 2008
This paper presents an efficient distributed genetic algorithm for classification rules extraction in data mining, which is based on a new method of dynamic data distribution applied to parallelism using networks of computers in order to mine large datasets. The presented algorithm shows many advantages when compared with other distributed algorithms proposed in the specific literature. In this way, some results are presented showing significant learning rate speed-up without compromising other features.