Applying sample weighting methods to genetic parallel programming

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

@InProceedings{Man:2003:Aswmtgpp,

• author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
• title = "Applying sample weighting methods to genetic parallel programming",
• booktitle = "Proceedings of the 2003 Congress on Evolutionary Computation CEC2003",
• editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and Tom Gedeon",
• pages = "928--935",
• year = "2003",
• publisher = "IEEE Press",
• address = "Canberra",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "8-12 " # dec,
• organisation = "IEEE Neural Network Council (NNC), Engineers Australia (IEAust), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
• keywords = "genetic algorithms, genetic programming, Boolean functions, Clocks, Computer science, Computer science education, Concurrent computing, Educational programs, Evolutionary computation, Parallel programming, Silicon compounds, Boolean functions, learning (artificial intelligence), parallel programming, Boolean function, DSW, GPP, SSW, UCI medical data classification database, class-equal SW method, dynamic SW method, equal SW method, evolutionary algorithm, genetic parallel programming, real-world system, sample weighting method, static SW method, training sample, training set",
• ISBN = "0-7803-7804-0",
• notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust, the EPS, and the IEE.",
• DOI = "doi:10.1109/CEC.2003.1299766",
• 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.",

}

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

Citations