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Genetic algorithm for continuous var...

Hourani, Mouin.

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Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems./
Author:	Hourani, Mouin.
Description:	86 p.
Notes:	Source: Masters Abstracts International, Volume: 42-05, page: 1752.
Contained By:	Masters Abstracts International42-05.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1418978
ISBN:	0496237519

Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems.
Hourani, Mouin.

Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems. - 86 p.

Source: Masters Abstracts International, Volume: 42-05, page: 1752.

Thesis (M.S.A.)--Miami University, 2004.

This study evaluates the efficacy of the continuous GA algorithm in solving two complex problems. The queuing network design problem has random processes and the decisions are subject to a cost constraint. Therefore, a simulation program is run to determine the fitness of each solution in population. The second problem is a large-scale gene-clustering problem in which thousands of genes have to be grouped into a fixed, small number of clusters on the basis of similarity. Clustering of gene expressions reduces the unmanageable volume of data into small number of sets that are of great interest to the biologists. For these problems, the GA algorithm produces good results compared to the alternative methods. The GA can also handle the same class of problems without the restrictive assumptions.

ISBN: 0496237519Subjects--Topical Terms:

626642
Computer Science.

Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems.
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100 1 $a Hourani, Mouin. $3 1935022
245 1 0 $a Genetic algorithm for continuous variable optimization with applications to queuing network and gene-clustering problems.
300 $a 86 p.
500 $a Source: Masters Abstracts International, Volume: 42-05, page: 1752.
500 $a Adviser: Mufit Ozden.
502 $a Thesis (M.S.A.)--Miami University, 2004.
520 $a This study evaluates the efficacy of the continuous GA algorithm in solving two complex problems. The queuing network design problem has random processes and the decisions are subject to a cost constraint. Therefore, a simulation program is run to determine the fitness of each solution in population. The second problem is a large-scale gene-clustering problem in which thousands of genes have to be grouped into a fixed, small number of clusters on the basis of similarity. Clustering of gene expressions reduces the unmanageable volume of data into small number of sets that are of great interest to the biologists. For these problems, the GA algorithm produces good results compared to the alternative methods. The GA can also handle the same class of problems without the restrictive assumptions.
590 $a School code: 0126.
650 4 $a Computer Science. $3 626642
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 0 $a Miami University. $3 1017534
773 0 $t Masters Abstracts International $g 42-05.
790 1 0 $a Ozden, Mufit, $e advisor
790 $a 0126
791 $a M.S.A.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1418978