NOW G-Net: learning classification programs on networks of workstations

The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to artificial intelligence, including data mining. In this paper, we present G-Net, a distributed evolutionary algorithm able to infer classifiers from precollected data. The main features of the system include robustness with respect to parameter settings, use of the minimum description length (MDL) criterion coupled with a stochastic search bias, coevolution as high-level control strategy, ability to face problems requiring structured representation languages, and suitability to parallel implementation on a network of workstations (NOW). Its parallel version, NOW G-Net, also described in this paper, is able to profitably exploit the computing power delivered by these platforms by incorporating a set of dynamic load distribution techniques that allow it to adapt to the variations of computing power arising typically in these systems. A proof-of-concept implementation based on PVM is used in the paper to demonstrate the effectiveness of NOW G-Net on a variety of datasets