Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{cheang:2003:gecco,

• author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
• title = "Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting",
• booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
• editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller",
• year = "2003",
• pages = "1802--1803",
• address = "Chicago",
• publisher_address = "Berlin",
• month = "12-16 " # jul,
• volume = "2724",
• series = "LNCS",
• ISBN = "3-540-40603-4",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, poster",
• DOI = "doi:10.1007/3-540-45110-2_72",
• abstract = "sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples 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 and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems.",
• notes = "GECCO-2003. A joint meeting of the twelfth International Conference on Genetic Algorithms (ICGA-2003) and the eighth Annual Genetic Programming Conference (GP-2003)",

}

Genetic Programming entries for Ivan Sin Man Cheang Kin-Hong Lee Kwong-Sak Leung

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