東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Knowledge guided evolutionary algori...

Wimmer, Hayden.

FindBook

Google Book

Amazon

博客來

Knowledge guided evolutionary algorithms in financial investing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Knowledge guided evolutionary algorithms in financial investing./
作者:	Wimmer, Hayden.
面頁冊數:	204 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3594636
ISBN:	9781303393037

Knowledge guided evolutionary algorithms in financial investing.
Wimmer, Hayden.

Knowledge guided evolutionary algorithms in financial investing. - 204 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--University of Maryland, Baltimore County, 2013.

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions---or traversing the search space---at random exhibits no control over how an organism evolves into a new organism that lies within the search space, also known as the mutation process. Employing codified human knowledge, the traversal of the search space may be constrained and directed thereby controlling mutation. This reveals the research question: How can codified human knowledge be integrated into the evolutionary mutation process in order to influence the traversal of the search space In order to investigate the research question, a design science approach will begin with a design artifact by creating DEFINE---Dynamic Evolution in Financial Investment Election. DEFINE employs decision trees as organisms and utilizes ontologies to constrain the mutation process. This research has the potential to reveal new and unexplored methods of employing human knowledge coupled with evolutionary computing to produce quality knowledge management systems.

ISBN: 9781303393037Subjects--Topical Terms:

626642
Computer Science.

Knowledge guided evolutionary algorithms in financial investing.
LDR:03333nam a2200301 4500 001 1959544
005 20140520124325.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303393037
035 $a (MiAaPQ)AAI3594636
035 $a AAI3594636
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wimmer, Hayden. $3 2094997
245 1 0 $a Knowledge guided evolutionary algorithms in financial investing.
300 $a 204 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Roy Rada.
502 $a Thesis (Ph.D.)--University of Maryland, Baltimore County, 2013.
520 $a A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions---or traversing the search space---at random exhibits no control over how an organism evolves into a new organism that lies within the search space, also known as the mutation process. Employing codified human knowledge, the traversal of the search space may be constrained and directed thereby controlling mutation. This reveals the research question: How can codified human knowledge be integrated into the evolutionary mutation process in order to influence the traversal of the search space In order to investigate the research question, a design science approach will begin with a design artifact by creating DEFINE---Dynamic Evolution in Financial Investment Election. DEFINE employs decision trees as organisms and utilizes ontologies to constrain the mutation process. This research has the potential to reveal new and unexplored methods of employing human knowledge coupled with evolutionary computing to produce quality knowledge management systems.
520 $a Preliminary experiments reveal an advantage, as measured by increased classification accuracy, to incorporating knowledge in the mutation process and also reveal financial markets follow a path of evolution with less than 10 generations to reach optimal fitness. Additional experiments conclude well-constructed knowledge constrains the mutation process to exercise control over the traversal of the search space whereas poorly-formed or random knowledge has no effect. Employing knowledge into the mutation process demonstrates making small changes in the organism's structure leads to small changes in the organism's function; however, making large changes showed mixed results. Performance, as measured by classification accuracy and number of generations to reach an optimum fitness, is improved by making small changes in the organism structure as opposed to random changes. This research demonstrates the number of years in time series data affects the number of generations required to achieve an optimum fitness. The methods explored in this research are applied to a financial investing scenario and profitability is shown to lie between two common investment strategies.
590 $a School code: 0434.
650 4 $a Computer Science. $3 626642
650 4 $a Information Technology. $3 1030799
650 4 $a Economics, Finance. $3 626650
690 $a 0984
690 $a 0489
690 $a 0508
710 2 $a University of Maryland, Baltimore County. $b Information Systems. $3 1034075
773 0 $t Dissertation Abstracts International $g 75-01B(E).
790 $a 0434
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3594636