A Parallel Approach for Evolutionary Induced Decision Trees. MPI plus OpenMP Implementation

One of the important and still not fully addressed issues in evolving decision trees is the induction time, especially for large datasets. In this paper, the authors propose a parallel implementation for Global Decision Tree system that combines shared memory (OpenMP) and message passing (MPI) paradigms to improve the speed of evolutionary induction of decision tree. The proposed solution is based on the classical master-slave model. The population is evenly distributed to available nodes and cores, and the time consuming operations like fitness evaluation and genetic operators are executed in parallel on slaves. Only the selection is performed on the master node. Efficiency and scalability of the proposed implementation is validated experimentally on artificial datasets. It shows noticeable speedup and possibility to efficiently process large datasets.