NUMERICAL NEGATIVE SELECTION ALGORITHM

2010 ◽  
Vol 19 (05) ◽  
pp. 703-712
Author(s):  
TAO CAI ◽  
SHIGUANG JU ◽  
DEJIAO NIU
Keyword(s):  
Negative Selection ◽  
Detection System ◽  
Immune Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Time And Space ◽  
Data Set ◽  
Matching Rule ◽  
Artificial Immune Algorithm ◽  
Negative Selection Algorithm

The artificial immune algorithm is the hot topic in much research such as the intrusion detection system, the information retrieval system and the data mining system. The negative selection algorithm is the typical artificial immune algorithm. A common representation of binary strings for antibody (detector) and antigen have been associated with inefficiencies when generating detector and inspecting antigen. We use a single integer to represent the detector and provide the basis of improving negative selection algorithm efficiency. In the detector generation algorithm, extracting sub-strings in self that its length is larger than threshold and converting them to single integer in numerical interval, then the rest integers in numerical interval are selected as numerical detectors. It can reduce the time and space overhead of detector generation and provide the facility to analyze the positive and negative errors when antigen inspection. The numerical matching rule is given. The B-tree is used to create index of numerical detector. Extracting sub-strings in antigen that its length is larger than threshold and converting them to some integers. If there is the same value as those integers in the index of numerical detector, then the antigen matches one numerical detector. It can improve the efficiency of antigen inspection. Finally the prototype of the numerical negative selection algorithm and negative selection algorithm are realized to test the overhead of the detector generation and antigen inspection using the live data set. The results show that the numerical negative selection algorithm can reduce the time and space overhead and avoid fluctuation of the overhead.

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

A new approach experimental to diagnosis of the failures in mechanical structures using the artificial immune algorithm with negative selection / Uma nova abordagem experimental para o diagnóstico das falhas nas estruturas mecânicas utilizando o algoritmo de imunidade artificial com seleção negativa

2021 ◽  
Vol 7 (7) ◽  
pp. 66372-66392
Author(s):  
Simone Silva Frutuoso de Souza ◽  
Mailon Bruno Pedri de Campos ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima
Keyword(s):  
Negative Selection ◽  
Immune Algorithm ◽  
Signal Acquisition ◽  
Artificial Immune ◽  
Sensors And Actuators ◽  
Artificial Immune Algorithm ◽  
Negative Selection Algorithm ◽  
Experimental Apparatus ◽  
Inspection Process ◽  
Beam Type

In this paper we present a new experimental approach to diagnose failures in mechanical structures using as decision tool an artificial immune algorithm with negative selection. This method is divided into two modules, and the acquisition and data processing module and analysis, detecting and characterizing flaws module. The module for data acquisition and processing of the experimental apparatus is constituted as sensors and actuators, so as to capture the signals in the structure and store it in the computer. From the signal acquisition executed if the negative selection algorithm to identify and characterize flaws in the structure. The main application of this methodology is to assist in the inspection process of mechanical structures in order to identify and characterize the flaws, as well as perform the decisions in order to avoid accidents. To evaluate the proposed methodology, experiments were performed in the laboratory where a real signs database was captured in a structure of the beam type, made of aluminum. The results obtained in the tests show robustness and efficiency when compared to literature.

scholarly journals MONITORAMENTO DE FALHAS EM SISTEMAS DINÂMICOS UTILIZANDO UM MÉTODO DE ANÁLISE DE DADOS BASEADO NO ALGORITMO DE SELEÇÃO NEGATIVA

2021 ◽  
Vol 13 (2) ◽  
pp. 78-87
Author(s):  
Driely Candido Santos ◽  
Mara Lúcia Martins Lopes ◽  
Fábio Roberto Chavarette ◽  
Bruno Ferreira Rossanês
Keyword(s):  
Negative Selection ◽  
Intelligent System ◽  
Artificial Immune Systems ◽  
Base Line ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Vibration Signals ◽  
Immune Systems ◽  
Negative Selection Algorithm

This work presents the application of a method for monitoring and diagnosing failures in mechanical structures based on the theory of vibration signals and on Artificial Immune Systems to assist in data processing. It uses the Negative Selection Algorithm as a tool to identify fault samples extracted from the laboratory simulated signals of a dynamic rotor. This methodology can help mechanical structure maintenance professionals, facilitating decision-making. The data set used in the processing of the intelligent system was generated through experiments. For normal (base-line) conditions, the signals of the rotor in free operation were used, that is, without the addition of unbalance mass, and for the fault conditions, unbalance masses were added to the system. The results are satisfactory, showing precision and robustness.

Negative Selection Algorithm Based on Double Matching Rules

2011 ◽  
Vol 204-210 ◽  
pp. 42-45 ◽  
Author(s):  
Yu Hu ◽  
Bin Li
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Negative Selection ◽  
Hamming Distance ◽  
Network Intrusion Detection ◽  
Selection Algorithm ◽  
Matching Rule ◽  
Negative Selection Algorithm ◽  
Network Intrusion ◽  
Matching Rules

The theory of artificial immune had been widely used in the research of network intrusion detection. Nowadays, the existing detector generating algorithms based on negative selection usually use a certain matching rule, as a result, too many detectors may generate, and the false alarm rate will become more serious. This paper proposes an improved negative selection algorithm using double matching rule: candidate detectors should be selected by the improved Hamming distance matching first, then the remaining detectors go through the segmented r-chunks(rch) matching rule. Experiments show that compared with traditional algorithms, this method brings a small number and more efficient detectors, reduces the false alarm rate and guarantees the efficiency of detectors.

AISTTP: AN ARTIFICIAL IMMUNE ALGORITHM TO SOLVE TRAVELING TOURNAMENT PROBLEMS

2012 ◽  
Vol 11 (01) ◽  
pp. 1250008 ◽  
Author(s):  
LESLIE PÉREZ CÁCERES ◽  
MARÍA CRISTINA RIFF
Keyword(s):  
Clonal Selection ◽  
Selection Procedure ◽  
Immune Algorithm ◽  
Clonal Selection Algorithm ◽  
Hard Problem ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Traveling Tournament Problem ◽  
Np Hard Problem

The traveling tournament problem is a well-known and important problem within the collective sports communities. This is a NP-hard problem which makes difficult finding quality solutions in a short amount of time. In this paper, we include new components to the Clonal Selection Algorithm to tackle the Traveling Tournament Problem. We introduce a new selection procedure focused on improving diversity of the selected cells set. The algorithm also uses a new move which is based on the team home/away patterns. We have tested the algorithm with well-known benchmarks of the problem, and the results obtained are very encouraging. A statistical analysis is also reported. The results obtained show a new application of an artificial immune algorithm based on the CLONALG framework.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

2014 ◽  
Vol 472 ◽  
pp. 544-549 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette ◽  
Simone Silva Frutuoso de Souza ◽  
Adriano dos Santos e Souza ◽  
Mara Lúcia Martins Lopes
Keyword(s):  
Negative Selection ◽  
Structural Integrity ◽  
Artificial Immune Systems ◽  
Structural Failure ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Negative Selection Algorithm ◽  
Identification And Characterization

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

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