%0 Conference Paper %1 Man:2003:Aswmtgpp %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 928--935 %T Applying sample weighting methods to genetic parallel programming %X We investigate the sample weighting effect on Genetic Parallel Programming (GPP). GPP evolves parallel programs to solve the training samples in a training set. Usually, the samples are captured directly from a real-world system. The distribution of samples in a training set can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation causing premature convergence. This paper presents 4 sample weighting (SW) methods, i.e. Equal SW, Class-equal SW, Static SW (SSW) and Dynamic SW (DSW). We evaluate the 4 methods on 7 training sets (3 Boolean functions and 4 UCI medical data classification databases). Experimental results show that DSW is superior in performance on all tested problems. In the 5-input Symmetry Boolean function experiment, SSW and DSW boost the evolutionary performance by 465 and 745 times respectively. Due to the simplicity and effectiveness of SSW and DSW, they can also be applied to different population-based evolutionary algorithms. %@ 0-7803-7804-0

@inproceedings{Man:2003:Aswmtgpp, abstract = {We investigate the sample weighting effect on Genetic Parallel Programming (GPP). GPP evolves parallel programs to solve the training samples in a training set. Usually, the samples are captured directly from a real-world system. The distribution of samples in a training set can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation causing premature convergence. This paper presents 4 sample weighting (SW) methods, i.e. Equal SW, Class-equal SW, Static SW (SSW) and Dynamic SW (DSW). We evaluate the 4 methods on 7 training sets (3 Boolean functions and 4 UCI medical data classification databases). Experimental results show that DSW is superior in performance on all tested problems. In the 5-input Symmetry Boolean function experiment, SSW and DSW boost the evolutionary performance by 465 and 745 times respectively. Due to the simplicity and effectiveness of SSW and DSW, they can also be applied to different population-based evolutionary 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/23f315afe6f7ae12188acde8957e8b54e/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 = {c584b696d7ad7149c57cf5c2d166cecb}, intrahash = {3f315afe6f7ae12188acde8957e8b54e}, 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 = {928--935}, 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 = {Applying sample weighting methods to genetic parallel programming}, year = 2003 }