Recent Advances in Artificial Immune Systems

2017 ◽  
pp. 92-114 ◽  
Author(s):  
Florin Popentiu Vladicescu ◽  
Grigore Albeanu
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Data Structures ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Computing Methods ◽  
Artificial Immune ◽  
Immune Systems ◽  
Methods And Techniques ◽  
Soft Computing Methods

The designers of Artificial Immune Systems (AIS) had been inspired from the properties of natural immune systems: self-organization, adaptation and diversity, learning by continual exposure, knowledge extraction and generalization, clonal selection, networking and meta-dynamics, knowledge of self and non-self, etc. The aim of this chapter, along its sections, is to describe the principles of artificial immune systems, the most representational data structures (for the representation of antibodies and antigens), suitable metrics (which quantifies the interactions between components of the AIS) and their properties, AIS specific algorithms and their characteristics, some hybrid computational schemes (based on various soft computing methods and techniques like artificial neural networks, fuzzy and intuitionistic-fuzzy systems, evolutionary computation, and genetic algorithms), both standard and extended AIS models/architectures, and AIS applications, in the end.

Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

2012 ◽  
pp. 371-387 ◽  
Author(s):  
Cagatay Catal ◽  
Soumya Banerjee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Metrics ◽  
Prediction Models ◽  
Artificial Immune Systems ◽  
Fault Prediction ◽  
Artificial Immune ◽  
Software Fault Prediction ◽  
Immune Systems ◽  
Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

THE MODEL AND ALGORITHM ARTIFICIAL IMMUNE SYSTEM

2016 ◽  
Vol 8 (3) ◽  
pp. 5-10
Author(s):  
Астахова ◽  
I. Astakhova ◽  
Ушаков ◽  
S. Ushakov
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Program Complex ◽  
Immune Systems ◽  
The Creation

In particular, models had only one type of cages , they applied V-lymphocytes. The distribution and a decentralization were the second feature for using artificial immune systems. This article is devoted to creation the artificial immune system (AIS), the creation model and algorithm of IIS is considered. The model for realization of a problem is consid-ered. Accuracy of calculations is compared to other methods, especially to neural networks. The structure of a program complex is described.

scholarly journals Multithreshold Segmentation Based on Artificial Immune Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Valentin Osuna-Enciso ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Humberto Sossa
Keyword(s):  
Initial Conditions ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Gaussian Function ◽  
Gaussian Mixture ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems ◽  
Gradient Based ◽  
Low Sensitivity

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

How Do We Evaluate Artificial Immune Systems?

2005 ◽  
Vol 13 (2) ◽  
pp. 145-177 ◽  
Author(s):  
Simon M. Garrett
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Danger Theory

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness’ and ‘effectiveness.’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory.’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

scholarly journals Application of Bio-inspired Metaheuristics in the Data Clustering Problem

2011 ◽  
Vol 14 (3) ◽  
Author(s):  
Thelma Elita Colanzi ◽  
Wesley Klewerton Guez Assunção ◽  
Aurora Trinidad Ramirez Pozo ◽  
Ana Cristina B. Kochem Vendramin ◽  
Diogo Augusto Barros Pereira ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Considerable Effort ◽  
Number Of Clusters ◽  
Immune Systems ◽  
Clustering Problem ◽  
Natural Groups

Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.

Evolving an Ensemble of Neural Networks Using Artificial Immune Systems

2008 ◽  
pp. 121-130 ◽  
Author(s):  
Bruno H. G. Barbosa ◽  
Lam T. Bui ◽  
Hussein A. Abbass ◽  
Luis A. Aguirre ◽  
Antônio P. Braga
Keyword(s):  
Neural Networks ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems
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