%0 Conference Paper %1 cheang:2003:edcpugpp %A Cheang, Sin Man %A Lee, Kin Hong %A Leung, Kwong Sak %B Proceedings of the 2003 Congress on Evolutionary Computation CEC2003 %C Canberra %D 2003 %E Sarker, Ruhul %E Reynolds, Robert %E Abbass, Hussein %E Tan, Kay Chen %E McKay, Bob %E Essam, Daryl %E Gedeon, Tom %I IEEE Press %K algorithms, genetic programming %P 248--255 %T Evolving data classification programs using genetic parallel programming %X 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) serialises 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 generalisation performance. The accuracies of these evolved classification programs are comparable to other existing classification algorithms. %@ 0-7803-7804-0

@inproceedings{cheang:2003:edcpugpp, abstract = {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) serialises 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 generalisation performance. The accuracies of these evolved classification programs are comparable to other existing classification algorithms.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Canberra}, author = {Cheang, Sin Man and Lee, Kin Hong and Leung, Kwong Sak}, biburl = {https://www.bibsonomy.org/bibtex/23db80ae8b33b8f98b7193995b6038f0b/brazovayeye}, booktitle = {Proceedings of the 2003 Congress on Evolutionary Computation CEC2003}, editor = {Sarker, Ruhul and Reynolds, Robert and Abbass, Hussein and Tan, Kay Chen and McKay, Bob and Essam, Daryl and Gedeon, Tom}, interhash = {3e1ed27b908b2feef5b7c4eb4b01336c}, intrahash = {3db80ae8b33b8f98b7193995b6038f0b}, isbn = {0-7803-7804-0}, keywords = {algorithms, genetic programming}, month = {8-12 December}, notes = {CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.}, organisation = {IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)}, pages = {248--255}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:37:36.000+0200}, title = {Evolving data classification programs using genetic parallel programming}, year = 2003 }