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AIMOS: Automated Inferential Multi-O...

Praharaj, Blake.

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AIMOS: Automated Inferential Multi-Objective Optimization System.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	AIMOS: Automated Inferential Multi-Objective Optimization System./
作者:	Praharaj, Blake.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	83 p.
附註:	Source: Masters Abstracts International, Volume: 56-02.
Contained By:	Masters Abstracts International56-02(E).
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10249184
ISBN:	9781369455151

AIMOS: Automated Inferential Multi-Objective Optimization System.
Praharaj, Blake.

AIMOS: Automated Inferential Multi-Objective Optimization System. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 83 p.

Source: Masters Abstracts International, Volume: 56-02.

Thesis (M.S.)--Southern Connecticut State University, 2017.

Many important modern engineering problems involve satisfying multiple objectives. Simultaneous optimization of these objectives can be difficult as they compete for the same set of any given resources. One way to solve multiple-objective optimization is with the use of genetic algorithms (GA's).

ISBN: 9781369455151Subjects--Topical Terms:

516317
Artificial intelligence.

AIMOS: Automated Inferential Multi-Objective Optimization System.
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520 $a Many important modern engineering problems involve satisfying multiple objectives. Simultaneous optimization of these objectives can be difficult as they compete for the same set of any given resources. One way to solve multiple-objective optimization is with the use of genetic algorithms (GA's).
520 $a One can break down the structure of these multi-objective genetic algorithms (MOGA's) into two different approaches. One approach is based on incorporating multiple objectives into a single fitness function which will evaluate how well a given solution solves the issue. The other approach uses multiple fitness functions, each representing a different objective, which when combined create a solution set of possible solutions to the problem. This project focuses on combining these approaches in order to make a hybrid model, which can benefit from combining the results of the previous two methods; incorporating a level of automation that allows for inference of a final solution based on different prioritization of each objective. This solution would not have been previously attainable by either standalone method.
520 $a This project is named the Automated Inferential Multi-Objective Optimization System (AIMOS), and it can be applied to a multitude of different problem types. In order to show its capabilities, AIMOS has been applied to a theoretical optimization problem used to measure the effectiveness of GA's.
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