Paper Info
Title
Improving evolvability of genetic parallel programming using dynamic sample weighting
Abstract
This paper investigates the 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.
Year
Venue
Keywords
2003
GECCO
dynamic sample weighting,training set,genetic parallel programming,standard gpp,class-equal sw,static sw,dynamic sw,sample weighting effect,equal sw,improving evolvability,training sample,sample weighting,genetics,premature convergence
Field
DocType
Volume
Mathematical optimization,Weighting,Premature convergence,Computer science,Evolvability,Parallel computing,Artificial intelligence,Machine learning
Conference
2724
ISSN
ISBN
Citations 
0302-9743
3-540-40603-4
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Sin Man Cheang1445.14
Kin Hong Lee2506.56
Kwong-Sak Leung31887205.58