Multiobjective Optimization and Artificial Immune Systems

2011 ◽  
pp. 1-21 ◽  
Author(s):  
Fabio Freschi ◽  
Carlos A. Coello Coello ◽  
Maurizio Repetto
Keyword(s):  
Immune System ◽  
Multiobjective Optimization ◽  
State Of The Art ◽  
Optimization Algorithms ◽  
Artificial Immune Systems ◽  
The State ◽  
Review Of The Literature ◽  
Artificial Immune ◽  
Immune Systems

This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be focused in the chapter, Artificial Immune Systems (AIS) have some intrinsic characteristics which make them well suited as multiobjective optimization algorithms. Following this basic idea, different implementations have been proposed in the literature. This chapter aims to provide a thorough review of the literature on multiobjective optimization algorithms based on the emulation of the immune system.

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

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.

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.

Financial Classification Using an Artificial Immune System

2011 ◽  
pp. 1525-1540
Author(s):  
Anthony Brabazon ◽  
Alice Delahunty ◽  
Dennis O’Callaghan ◽  
Peter Keenan ◽  
Michael O’Neill
Keyword(s):  
Immune System ◽  
Classification System ◽  
Artificial Immune Systems ◽  
Dynamic Environment ◽  
Artificial Immune ◽  
Biologically Inspired ◽  
Immune Systems ◽  
Corporate Failure ◽  
Out Of Sample ◽  
Biologically Inspired Algorithms

Recent years have seen a dramatic increase in the application of biologically-inspired algorithms to business problems. Applications of neural networks and evolutionary algorithms have become common. However, as yet there have been few applications of artificial immune systems (AIS), algorithms that are inspired by the workings of the natural immune system. The natural immune system can be considered as a distributed, self-organizing, classification system that operates in a dynamic environment. The mechanisms of natural immune systems, including their ability to distinguish between self and non-self, provides a rich metaphorical inspiration for the design of pattern-recognition algorithms. This chapter introduces AIS and provides an example of how an immune algorithm can be used to develop a classification system for predicting corporate failure. The developed system displays good classification accuracy out-of-sample, up to two years prior to failure.

Artificial Immune Systems for IT-Security (Künstliche Immunsysteme für IT-Sicherheit)

2006 ◽  
Vol 48 (3) ◽  
Author(s):  
Thomas Stibor ◽  
Claudia Eckert ◽  
Jonathan Timmis
Keyword(s):  
Information Processing ◽  
Immune System ◽  
Artificial Immune Systems ◽  
Processing System ◽  
Database Security ◽  
Network Intrusion Detection ◽  
It Security ◽  
Artificial Immune ◽  
Immune Systems ◽  
Network Intrusion

SummaryThe immune system is an impressive information processing system with many appealing properties for solving problems. Artificial immune systems are a paradigm inspired by the immune system and are used for solving computational and information processing problems. In this paper, we outline two different immune-inspired approaches typically used for IT-security problems. Specifically, we present one of the first proposed immune inspired approaches for network intrusion detection, this is then complimented with an overview of recent investigations on the latest immunological theories and how they may be exploited in IT-security. We then present an artificial immune system concept for database security which encompasses issues such as confidentiality of database information and prevention of privacy-preserving data mining.

IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 21 (06) ◽  
pp. 1250031 ◽  
Author(s):  
MUHAMMAD ROZI MALIM ◽  
FARIDAH ABDUL HALIM
Keyword(s):  
Immune System ◽  
Network Theory ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Selection Mechanism ◽  
Artificial Immune ◽  
Time Line ◽  
Immune Systems

Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.

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