Independent Task Scheduling by Artificial Immune Systems, Differential Evolution, and Genetic Algorithms

2012 ◽  
Author(s):  
Pavel Kromer ◽  
Jan Plato ◽  
V'clav Snasel
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Task Scheduling ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Independent Task

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.

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.

Self-adaptive evolutionary image registration using differential evolution and artificial immune systems

2012 ◽  
Vol 33 (16) ◽  
pp. 2065-2070 ◽  
Author(s):  
José Santamaría ◽  
Sergio Damas ◽  
José M. García-Torres ◽  
Oscar Cordón
Keyword(s):  
Image Registration ◽  
Differential Evolution ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Self Adaptive

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.

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