Applying sample weighting methods to genetic parallel programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
  title =        "Applying sample weighting methods to genetic parallel
  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