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Immune programming.

Lau, Adriel.

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Immune programming.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Immune programming./
作者:	Lau, Adriel.
面頁冊數:	115 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0519.
Contained By:	Dissertation Abstracts International71-01B.
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR55819
ISBN:	9780494558195

Immune programming.
Lau, Adriel.

Immune programming. - 115 p.

Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0519.

Thesis (Ph.D.)--University of Alberta (Canada), 2009.

This thesis presents research on Artificial Immune Systems (AIS) and their applications. Particularly, the evolutionary algorithm of clonal selection which takes its inspiration from the human immune system is investigated. It is demonstrated that this algorithm leads to self-organization and evolution in an AIS. A novel paradigm, Immune Programming (IP), developed upon this algorithm is proposed. This proposed paradigm is capable of solving a wide range of domain-independent problems. Sample problems are defined and solved, demonstrating the effectiveness and excellent convergent qualities of this algorithm.

ISBN: 9780494558195Subjects--Topical Terms:

1669061
Engineering, Computer.

Immune programming.
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502 $a Thesis (Ph.D.)--University of Alberta (Canada), 2009.
520 $a This thesis presents research on Artificial Immune Systems (AIS) and their applications. Particularly, the evolutionary algorithm of clonal selection which takes its inspiration from the human immune system is investigated. It is demonstrated that this algorithm leads to self-organization and evolution in an AIS. A novel paradigm, Immune Programming (IP), developed upon this algorithm is proposed. This proposed paradigm is capable of solving a wide range of domain-independent problems. Sample problems are defined and solved, demonstrating the effectiveness and excellent convergent qualities of this algorithm.
520 $a In this thesis, components of the human immune system crucial to the improvement of AIS are identified, and implementations of these components in design principles for a proposed AIS model are presented and discussed. An innovative, probabilistic clonal selection algorithm, which combines the properties of these components such as the maturation process, cloning, hypermutation, and proliferation, is invented. Applications of the proposed algorithms to IP for symbolic regression and biological process modeling are illustrated. The effects of parameters on the behavior of the algorithm are analyzed. Results and performance of the algorithm are compared to the more conventional approaches of artificial neural networks and genetic programming algorithms.
520 $a The obtained results support the vision of this thesis that an artificial immune algorithm harnessing crucial components of the human immune system demonstrates superb adaptability to difficult situations and exceptional competence in dynamically generating solutions to complex problems.
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