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.

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.

A FUZZY INFERENCE SYSTEM TO DETERMINE THE NUMBER OF CLONES IN A CLASS OF ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 11 (01) ◽  
pp. 1250005
Author(s):  
LUIZ ANTONIO CARRARO ◽  
LEANDRO NUNES DE CASTRO ◽  
ANGELITA MARIA DE RE ◽  
FABRĹCIO OLIVETTI DE FRANÇA
Keyword(s):  
Immune Response ◽  
Immune Cell ◽  
Fuzzy Inference ◽  
Clonal Selection ◽  
Selection Process ◽  
Artificial Immune Systems ◽  
Reproduction Rate ◽  
Artificial Immune ◽  
Inference System ◽  
Immune Systems

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

Cost optimization of water distribution networks by using artificial immune systems

2014 ◽  
Vol 64 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Miraç Eryiğit
Keyword(s):  
Water Distribution ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Proposed Model ◽  
The Impact

This study aims at the development of an optimization model based on artificial immune systems (AIS) to minimize cost designs of water distribution networks (WDNs). Clonal selection algorithm (Clonalg), a class of AIS, was used as an optimization technique in the model, and its mutation operation was modified to increase the diversity (search capability). EPANET, a widely known WDN simulator, was used in conjunction with the proposed model. The model was applied to four WDNs of Two-loop, Hanoi, Go Yang, New York City, and the results obtained were compared with other heuristic and mathematical optimization models in the related literature, such as harmony search, genetic algorithm, immune algorithm, shuffled complex evolution, differential evolution, and non-linear programming-Lagrangian algorithm. Furthermore, the modified Clonalg was compared with the classic Clonalg in order to demonstrate the impact of the modification on the diversity. The proposed model appeared to be promising in terms of cost designs of WDNs.

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.

Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune Systems

2009 ◽  
Vol 25 (25) ◽  
pp. 159-164 ◽  
Author(s):  
Mikhail Gorobetz ◽  
Ivars Alps ◽  
Anatoly Levchenkov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Artificial Immune Systems ◽  
Optimal Schedule ◽  
Electric Transport ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems

Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune SystemsThis paper describes mathematical formulation and application of artificial immune system for scheduling tasks for public electric transport. Artificial immune system is inspired by human immune system to simulate the process of interaction between antigens and antibodies. The task of scheduling in transport system is represented as one of the most well-known flow shop problem. Artificial immune system as a genetic based method is used to solve such task. Mathematical model and algorithm is proposed to create optimal schedule for public electric transport for minimization of electric energy consumption and time. Numerical example shows several steps of algorithm for artificial immune system for scheduling task solution.

Applications of Artificial Immune Systems in Agents

2011 ◽  
pp. 99-122
Author(s):  
Luis Fernando Niño Vasquez ◽  
Fredy Fernando Muñoz Mopan ◽  
Camilo Eduardo Prieto Salazar ◽  
José Guillermo Guarnizo Marín
Keyword(s):  
Immune Response ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Multi Agent Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Agent Systems ◽  
Adaptive Immune ◽  
Multi Agent ◽  
Object Transportation

Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high efficacy. This is a survey chapter within which single and multi-agent systems inspired by immunology concepts are presented and analyzed. Most of the work is usually based on the adaptive immune response characteristics, such as clonal selection, idiotypic networks, and negative selection. However, the innate immune response has been neglected and there is not much work where innate metaphors are used as inspiration source to develop robotic systems. Therefore, a work that involves some interesting features of the innate and adaptive immune responses in a cognitive model for object transportation is presented at the end of this chapter.

Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

2010 ◽  
Vol 143-144 ◽  
pp. 547-551
Author(s):  
Zhe Lian
Keyword(s):  
Optimization Algorithm ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Computation Method ◽  
Clonal Selection Algorithm ◽  
Genetic Optimization ◽  
Selection Algorithm ◽  
Human Immune System ◽  
Immune Systems ◽  
Genetic Optimization Algorithm

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic Algorithm (GA) on performance.

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