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Neural enhancement for multiobjectiv...

Auburn University.

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Neural enhancement for multiobjective optimization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neural enhancement for multiobjective optimization./
作者:	Garrett, Aaron.
面頁冊數:	219 p.
附註:	Adviser: Gerry Dozier.
Contained By:	Dissertation Abstracts International69-06B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3317313
ISBN:	9780549668107

Neural enhancement for multiobjective optimization.
Garrett, Aaron.

Neural enhancement for multiobjective optimization. - 219 p.

Adviser: Gerry Dozier.

Thesis (Ph.D.)--Auburn University, 2008.

In this work, a neural network approach is applied to multiobjective optimization problems in order to expand the set of optimal solutions. The network is trained using results obtained from existing evolutionary multiobjective optimization approaches. The network is then evaluated based on its performance against those same approaches when given more processing time. The results are collected from a set of well-known benchmark multiobjective problems, and its performance is evaluated using various indicators from the multiobjective optimization literature.

ISBN: 9780549668107Subjects--Topical Terms:

626642
Computer Science.

Neural enhancement for multiobjective optimization.
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502 $a Thesis (Ph.D.)--Auburn University, 2008.
520 $a In this work, a neural network approach is applied to multiobjective optimization problems in order to expand the set of optimal solutions. The network is trained using results obtained from existing evolutionary multiobjective optimization approaches. The network is then evaluated based on its performance against those same approaches when given more processing time. The results are collected from a set of well-known benchmark multiobjective problems, and its performance is evaluated using various indicators from the multiobjective optimization literature.
520 $a Preliminary experiments reveal the viability of this approach for expanding the set of solutions to multiobjective problems. Further experiments prove that it is possible to train the neural network in a reasonable time using heuristic methods. The results of this training approach are shown to be very competitive with the underlying evolutionary multiobjective optimization approach that was used to produce the training set. Additional experiments reveal the applicability of this approach across existing multiobjective optimization approaches.
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