東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

AIMOS: Automated Inferential Multi-O...

Praharaj, Blake.

Linked to FindBook

Google Book

Amazon

博客來

AIMOS: Automated Inferential Multi-Objective Optimization System.

Record Type:	Electronic resources : Monograph/item
Title/Author:	AIMOS: Automated Inferential Multi-Objective Optimization System./
Author:	Praharaj, Blake.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	83 p.
Notes:	Source: Masters Abstracts International, Volume: 56-02.
Contained By:	Masters Abstracts International56-02(E).
Subject:	Artificial intelligence. -
Online resource:	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.
LDR:02352nmm a2200325 4500 001 2126428
005 20171121080732.5
008 180830s2017 ||||||||||||||||| ||eng d
020 $a 9781369455151
035 $a (MiAaPQ)AAI10249184
035 $a AAI10249184
040 $a MiAaPQ $c MiAaPQ
100 1 $a Praharaj, Blake. $3 3288528
245 1 0 $a AIMOS: Automated Inferential Multi-Objective Optimization System.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 83 p.
500 $a Source: Masters Abstracts International, Volume: 56-02.
500 $a Adviser: Hrvoje Podnar.
502 $a Thesis (M.S.)--Southern Connecticut State University, 2017.
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.
590 $a School code: 0928.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Logic. $3 529544
690 $a 0800
690 $a 0984
690 $a 0395
710 2 $a Southern Connecticut State University. $b Computer Science. $3 2103594
773 0 $t Masters Abstracts International $g 56-02(E).
790 $a 0928
791 $a M.S.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10249184