Evolving data classification programs using genetic parallel programming

A novel Linear Genetic Programming (Linear GP) paradigm called Genetic Parallel Programming (GPP) has been proposed to evolve parallel programs based on a Multi-ALU Processor. The GPP Accelerating Phenomenon, i.e. parallel programs are easier to be evolved than sequential programs, opens up a new two-step approach: 1) evolves a parallel program solution; and 2) serializes the parallel program to a equivalent sequential program. In this paper, five two-class UCI Machine Learning Repository databases are used to investigate the effectiveness of GPP. The main advantages to employ GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; 2) discovering parallel algorithms automatically; and 3) boosting evolutionary performance by the GPP Accelerating Phenomenon. Experimental results show that GPP evolves simple classification programs with good generalization performance. The accuracies of these evolved classification programs are comparable to other existing classification algorithms.