DocumentCode :
3003249
Title :
Using singular value decomposition to improve a genetic algorithm´s performance
Author :
Martin, Jacob G. ; Rasheed, Khaled
Author_Institution :
Comput. Sci., Georgia Univ., sAthens, GA, USA
Volume :
3
fYear :
2003
fDate :
8-12 Dec. 2003
Firstpage :
1612
Abstract :
The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of genetic algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way that provides both a saving in storage space and an improvement in information retrieval. It will be shown that these beneficial properties can be extended to many other different types of comparison as well. Briefly, vectors representing the genes of individuals are projected into a new low-dimensional space, obtained by the singular value decomposition of a gene-individual matrix. The information about what it means to be a good or bad individual serves as a basis for qualifying candidate individuals for reinsertion into the next generation. Positive results from different approaches of this application are presented and evaluated. In addition, several possible alternative techniques are proposed and considered.
Keywords :
genetic algorithms; singular value decomposition; SVD; document comparison; genes; genetic algorithm; information modeling; information retrieval; linear algebra; singular value decomposition; storage space; Autocorrelation; Chemical analysis; Computer science; Genetics; Information retrieval; Jacobian matrices; Large scale integration; Linear algebra; Matrix decomposition; Singular value decomposition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN :
0-7803-7804-0
Type :
conf
DOI :
10.1109/CEC.2003.1299865
Filename :
1299865
Link To Document :
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