Evolving data classification programs using genetic parallel programming
Sin Man Cheang, Kin-Hong Lee, Kwong‐Sak Leung · 2003
A novel linear genetic programming (LGP) paradigm called genetic parallel programming (GPP) has been proposed to evolve parallel programs based on a multi-ALU processor. It is found that GPP can evolve parallel programs for data classification problems. In this paper, five binary-class UCI machine learning repository databases are used to test the effectiveness of the proposed GPP-classifier. The main advantages of employing GPP for data classification are: 1) speeding up evolutionary process by parallel hardware fitness evaluation; and 2) discovering parallel algorithms automatically. Experimental results show that the GPP-classifier evolves simple classification programs with good generalization performance. The accuracies of these evolved classifiers are comparable to other existing classification algorithms.