Improving Evolvability of Genetic Parallel Programming
Using Dynamic Sample Weighting
S. Cheang, K. Lee, and K. Leung. Genetic and Evolutionary Computation -- GECCO-2003, volume 2724 of LNCS, page 1802--1803. Chicago, Springer-Verlag, (12-16 July 2003)
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.
Genetic and Evolutionary Computation -- GECCO-2003
year
2003
month
12-16 July
pages
1802--1803
publisher
Springer-Verlag
series
LNCS
volume
2724
publisher_address
Berlin
isbn
3-540-40603-4
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)
%0 Conference Paper
%1 cheang:2003:gecco
%A Cheang, Sin Man
%A Lee, Kin Hong
%A Leung, Kwong Sak
%B Genetic and Evolutionary Computation -- GECCO-2003
%C Chicago
%D 2003
%E Cantú-Paz, E.
%E Foster, J. A.
%E Deb, K.
%E Davis, D.
%E Roy, R.
%E O'Reilly, U.-M.
%E Beyer, H.-G.
%E Standish, R.
%E Kendall, G.
%E Wilson, S.
%E Harman, M.
%E Wegener, J.
%E Dasgupta, D.
%E Potter, M. A.
%E Schultz, A. C.
%E Dowsland, K.
%E Jonoska, N.
%E Miller, J.
%I Springer-Verlag
%K algorithms, genetic poster programming,
%P 1802--1803
%T Improving Evolvability of Genetic Parallel Programming
Using Dynamic Sample Weighting
%V 2724
%X 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.
%@ 3-540-40603-4
@inproceedings{cheang:2003:gecco,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Chicago},
author = {Cheang, Sin Man and Lee, Kin Hong and Leung, Kwong Sak},
biburl = {https://www.bibsonomy.org/bibtex/23de4759b27d3ccb4c6cac384fa291403/brazovayeye},
booktitle = {Genetic and Evolutionary Computation -- GECCO-2003},
editor = {Cant{\'u}-Paz, E. and Foster, J. A. and Deb, K. and Davis, D. and Roy, R. and O'Reilly, U.-M. and Beyer, H.-G. and Standish, R. and Kendall, G. and Wilson, S. and Harman, M. and Wegener, J. and Dasgupta, D. and Potter, M. A. and Schultz, A. C. and Dowsland, K. and Jonoska, N. and Miller, J.},
interhash = {f42414cc7a2e11147346e8f94a6f7339},
intrahash = {3de4759b27d3ccb4c6cac384fa291403},
isbn = {3-540-40603-4},
keywords = {algorithms, genetic poster programming,},
month = {12-16 July},
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)},
pages = {1802--1803},
publisher = {Springer-Verlag},
publisher_address = {Berlin},
series = {LNCS},
timestamp = {2008-06-19T17:37:36.000+0200},
title = {Improving Evolvability of Genetic Parallel Programming
Using Dynamic Sample Weighting},
volume = 2724,
year = 2003
}