The Clonal-Selection Theory

Ada, G. L.; Nossal, G. J. V.

doi:10.1038/scientificamerican0887-62

Cited by 187 publications

(94 citation statements)

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“…During maturation, all T-cells that recognize self-antigen are excluded from population of T-cells this process is termed as negative selection (Nossal, 1994). If a B-cell interacts with a non-self antigen with affinity threshold; it proliferates and differentiates in the memory and effecter cells, this process is termed as clonal selection (Ada and Nossal, 1987). In contrast, if a B-cell recognizes a self-antigen, it might results in suppression as recommended by the immune network theory (Jerne, 1974).…”

Section: Clonal Algorithmmentioning

confidence: 99%

An Intelligent Decision Support System for Workforce Forecast

Wan¹,

Chen²,

Kuriger³

2011

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information , 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) 05-01-2011 REPORT TYPE 7184D214 PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERThe University of Texas at San Antonio One UTSA Circle San Antonio TX 78249 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)Air SPONSOR/MONITOR'S REPORT NUMBER(S)AFRL-RH-WP-TR-2011-0019 DISTRIBUTION / AVAILABILITY STATEMENTDistribution A: Approved for public release; distributed is unlimited. SUPPLEMENTARY NOTES88ABW/PA cleared on 23 Feb 2011, 88ABW-2011-0727. ABSTRACTThe main objective of this project is to enhance the effectiveness of workforce forecast and deployment through innovative approaches of artificial intelligence. The research included a final document of conventional and innovative methods of workforce forecasting and a decision support software program incorporating advanced artificial intelligence techniques for workforce forecasting. SUBJECT TERMS ABSTRACTThis project studies workforce forecasting in two main aspects: (1) an extensive review of the existing methodologies and techniques and (2) an effort to develop a decision support system with models and software programming. The main objective of this project is to enhance the effectiveness of workforce forecasting and deployment through innovative approaches of artificial intelligence. The deliverables include a summary report of conventional and innovative methods of workforce forecasting (Part I) and a decision support software program using artificial intelligence techniques for workforce forecasting (Part II).Part I, provided a thorough literature review of fundamental research and practices of demand and supply forecasting techniques for workforce analysis. Over 300 relevant literatures have been identified and reviewed by the project team, and 289 of them are covered in this report.Following is a summary of the main contents and contributions of Part I:• This report provides an extensive review of the fundamental knowledge, applications, guidelines, and scope of use of various workforce forecasting methods.• A decision tree has been proposed for selecting an appropriate forecasting ...

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Section: Clonal Algorithmmentioning

confidence: 99%

An Intelligent Decision Support System for Workforce Forecast

Wan¹,

Chen²,

Kuriger³

2011

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“…They suppress the growth of these dominant antibodies so as to diversify the types of antibodies that are tested against the antigens in the exploring and exploiting different results. Physiological immune systems have affinity maturation mechanism and the immune selection mechanism [7]. Affinity maturation conduces to immune system self-regulates the production of antibodies and diverse antibodies, these higher-affinity matured cells are then selected to enter the pool of memory cells.…”

Section: Immune Systems Mechanism Analysismentioning

confidence: 99%

Studying the Performance of Quantum Evolutionary Algorithm Based on Immune Theory

You

Liu

Shuai

2007

Computational Science – ICCS 2007

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Abstract.A novel quantum evolutionary algorithm based on immune operator (MQEA) is proposed. The algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, immune cell can accomplish cross-mutation and Self-adaptive mutation, memory cells can be produced and similar antibodies can be suppressed. It not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed. The technique for improving the performance of MQEA has been described and its superiority is shown by some simulation experiments in this paper.

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“…AIS algorithm simulates human body's defense system against viruses [15]. Our adaptive immune system produces antibodies (Ab) whose aim is to bind to any foreign molecule, antigen (Ag), that can be recognized by immune system.…”

Section: Ais Algorithm and Clonal Selection Principlesmentioning

confidence: 99%

Application of Artificial Immune System Algorithm to Electromagnetics Problems

Kıłıc

2010

PIER B

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Abstract-This paper investigates the use of clonal selection principles based on our immune system for optimization applications in electromagnetics. This concept is based on our immune system's ability to respond to an antigen and produce a pool of anti-body secreting cells. In addition to the common implementations of this algorithm where the affinity maturation and cloning principles of clonal selection principles are used, we utilize memory and the cross-over concepts that are common to other bio-inspired methods. The performance of the algorithm is investigated for well known mathematical test functions and its potential is demonstrated in the context of the design of a radar absorbing material and a planar phased array antenna with specific radiation and null characteristics.

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The Clonal-Selection Theory

Cited by 187 publications

References 0 publications

An Intelligent Decision Support System for Workforce Forecast

An Intelligent Decision Support System for Workforce Forecast

Studying the Performance of Quantum Evolutionary Algorithm Based on Immune Theory

Application of Artificial Immune System Algorithm to Electromagnetics Problems

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