Hybrid parallelization of evolutionary model tree induction

Abstract

This paper illustrates a parallel implementation of evolutionary induction of model trees. An objective is to demonstrate that such evolutionary evolved trees, which are emerging alternatives to the greedy top-down solutions, can be successfully applied to large scale data. The proposed approach combines message passing (MPI) and shared memory (OpenMP) paradigms. This hybrid approach is based on a classical master-slave model in which the individuals from the population are evenly distributed to available nodes and cores. The most time consuming operations like recalculation of the regression models in the leaves as well as the fitness evaluation and genetic operators are executed in parallel on slaves. Experimental validation on artificial and real-life datasets confirms the efficiency of the proposed implementation.