Showing papers on "Artificial immune system published in 2006"

Advances in artificial immune systems

Abstract: During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity

An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery

Abstract: A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out.

A new method to medical diagnosis: Artificial immune recognition system (AIRS) with fuzzy weighted pre-processing and application to ECG arrhythmia

Abstract: Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. Artificial immune systems (AISs) is a new but effective branch of artificial intelligence. Among the systems proposed in this field so far, artificial immune recognition system (AIRS), which was proposed by A. Watkins, has showed an effective and intriguing performance on the problems it was applied. Previously, AIRS was applied a range of problems including machine-learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification problems. The conducted medical classification task was performed for ECG arrhythmia data taken from UCI repository of machine-learning. Firsly, ECG dataset is normalized in the range of [0,1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can be improved by ours, is a new method and firstly, it is applied to ECG dataset. Classifier system consists of three stages: 50–50% of traing-test dataset, 70–30% of traing-test dataset and 80–20% of traing-test dataset, subsequently, the obtained classification accuries: 78.79, 75.00 and 80.77%.

Dendritic Cells for Anomaly Detection

Abstract: Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Danger is ubiquitous: detecting malicious activities in sensor networks using the dendritic cell algorithm

Abstract: There is a list of unique immune features that are currently absent from the existing artificial immune systems and other intelligent paradigms. We argue that some of AIS features can be inherent in an application itself, and thus this type of application would be a more appropriate substrate in which to develop and integrate the benefits brought by AIS. We claim here that sensor networks are such an application area, in which the ideas from AIS can be readily applied. The objective of this paper is to illustrate how closely a Danger Theory based AIS – in particular the Dendritic Cell Algorithm matches the structure and functional requirements of sensor networks. This paper also introduces a new sensor network attack called an Interest Cache Poisoning Attack and discusses how the DCA can be applied to detect this attack.

VIS: An artificial immune network for multi-objective optimization

Abstract: The aim of this work is to propose and validate a novel multi-objective optimization algorithm based on the emulation of the behaviour of the immune system The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multi-objective evolutionary algorithms described in the literature, such as diversity preservation, memory, adaptivity, and elitism The proposed approach is compared with three multi-objective evolutionary algorithms that are representative of the state of the art in multi-objective optimization Algorithms are tested on six standard problems (both unconstrained and constrained) and comparisons are carried out using three different metrics Results show that the proposed approach has very good performances and can become a valid alternative to standard algorithms for solving multi-objective optimization problems

libtissue - implementing innate immunity

Abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.

Solving job shop scheduling problems using artificial immune system

Abstract: The n-job, m-machine job shop scheduling (JSS) problem is one of the general production scheduling problems. Many existing heuristics give solutions for small size problems with near optimal solutions. This paper deals with the criterion of makespan minimization for the job shop scheduling of different size problems. The proposed computational method of artificial immune system algorithm (AIS) is used for finding optimal makespan values of different size problems. The artificial immune system algorithm is tested with 130 benchmark problems [10 (ORB1-ORB5 & ARZ5-ARZ9), 40 (LA01-LA40) and 80 (TA01-TA80)]. The results show that the AIS algorithm is an efficient and effective algorithm which gives better results than the Tabu search shifting bottleneck procedure (TSSB) as well as the best solution of shifting bottleneck procedure ( SB-GLS1 ) of Balas and Vazacopoulos.

Assembly planning using a novel immune approach

Abstract: Inspired by the vertebrate immune system, artificial immune system (AIS) has emerged as a new branch of computational intelligence. This paper explores the application of AIS in the problem of assembly planning and proposes a novel approach, called the immune optimization approach (IOA), to generate the optimal assembly plan. Based on the bionic principles of AIS, IOA introduces manifold immune operations including immune selection, clonal selection, inoculation and immune metabolism to derive the optimal assembly sequence. Maintenance of population diversity, attention to the local as well as the global search, and employment of heuristic knowledge to direct the search of optimized assembly sequences are the major concerns of IOA. The details of IOA are presented and the immune operations are discussed. Two practical products are taken as examples to illustrate the validity of IOA in assembly planning, and encouraging solutions in quality and efficiency are achieved. Comparisons with genetic algorithm demonstrate that IOA finds the optimal assembly solution or near-optimal ones more reliably and more efficiently, indicating that IOA has potential and advantages in dealing with assembly planning.

On the use of hyperspheres in artificial immune systems as antibody recognition regions

Abstract: Using hyperspheres as antibody recognition regions is an established abstraction which was initially proposed by theoretical immunologists for use in the modeling of antibody-antigen interactions. This ion is also employed in the development of many artificial immune system algorithms. Here, we show several undesirable properties of hyperspheres, especially when operating in high dimensions and discuss the problems of hyperspheres as recognition regions and how they have affected overall performance of certain algorithms in the context of real-valued negative selection.

A modified immune network algorithm for multimodal electromagnetic problems

Abstract: Some optimization algorithms based on theories from immunology have the feature of finding an arbitrary number of optima, including the global solution. However, this advantage comes at the cost of a large number of objective function evaluations, in most cases, prohibitive in electromagnetic design. This paper proposes a modified version of the artificial immune network algorithm (opt-AINet) for electromagnetic design optimization. The objective of this modified AINet (m-AINet) is to reduce the computational effort required by the algorithm, while keeping or improving the convergence characteristics. Another improvement proposed is to make it more suitable for constrained problems through the utilization of a specific constraint-handling technique. The results obtained over an analytical problem and the design of an electromagnetic device show the applicability of the proposed algorithm

Psycho-Clonal algorithm based approach to solve continuous flow shop scheduling problem

Abstract: This research presents a novel approach to solve m-machine no-wait flow shop problem. A continuous flow shop problem with total flow time as criterion is considered. This paper extends the artificial immune system (AIS) approach by proposing a new methodology termed as Psycho-Clonal algorithm. Proposed algorithm enjoys the flavor of AIS and Maslow's need hierarchy theory to evolve a meta heuristic. Numerical simulation with small and large number of jobs with respect to error percentage is reported. The results obtained are compared with the other existing approaches. Numerical simulation has revealed that results obtained using proposed algorithm have significant improvement over others.

Optimal approximation of linear systems by artificial immune response

Abstract: This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

On the use of hyperspheres in artificial immune systems as antibody recognition regions

Abstract: Using hyperspheres as antibody recognition regions is an established abstraction which was initially proposed by theoretical immunologists for use in the modeling of antibody-antigen interactions. This abstraction is also employed in the development of many artificial immune system algorithms. Here, we show several undesirable properties of hyperspheres, especially when operating in high dimensions and discuss the problems of hyperspheres as recognition regions and how they have affected overall performance of certain algorithms in the context of real-valued negative selection.

A population adaptive based immune algorithm for solving multi-objective optimization problems

Abstract: The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.

Negative selection algorithms: from the thymus to v-detector

Abstract: Artificial Immune Systems (AIS) is a research area of developing computational methods inspired by biological immune systems. The approach of negative selection algorithms (NSA) is one of the major models of AIS. This dissertation does a comprehensive survey of NSA and highlights the key components that define a negative selection algorithm. It demonstrates that the so-called 'negative selection algorithms' have been a very broad interpretation compared with its biological archetype and differ from one another in strategy, applicability and implementation. This work proposed a new negative selection algorithm called V-detector. It has several important features that alleviate some difficulties in negative selection algorithms. (1) Statistical techniques are integrated in the detector generation process to estimate the detector coverage. (2) Detectors with variable coverage are used in a highly efficient manner to achieve maximum coverage. (3) A boundary-aware algorithm is proposed to interpret the training data set as a whole, instead of considering them as independent points. It shows that negative selection's certain learning property cannot be replaced by straightforward positive selection. (4) The main components of V-detector can be customized for different data/detector representations and detector generation mechanisms. This generic characteristic could connect the gap between different negative selection algorithms. For example, extension from Euclidean distance to more general distance measures demonstrated its potential to accommodate domain specific elements. (5) One-shot training instead of evolutionary approach is utilized to lead to a more concise model. While it doesn't exclude combination or cooperation with evolutionary process, this simple model makes it possible to implement a very efficient learning process and provides great flexibility for extension. In the light of recent years' doubts about negative selection algorithms, applicability of negative selection algorithms is discussed in details both to understand the reasonable scenarios to use it and its intrinsic limitations. Negative selection algorithms, mainly MILA (Multilevel Immune Learning Algorithm) and V-detector, were experimented on various real-world datasets. To demonstrate its strength, V-detector was used in image-based dental diagnosis with a novel real-valued representation of occlusion condition on dental images.

On the appropriateness of negative selection for anomaly detection and network intrusion detection

Abstract: The immune system is a complex system which protects humans and animals against diseases caused by foreign intruders such as viruses, bacteria and fungi It appears as if the recognition and protection mechanism of the immune system can lead to the development of novel concepts and techniques for detecting intrusions in computer networks, particularly in the area of anomaly detection In this thesis, the principle of "negative selection" as a paradigm for detecting intrusions in computer networks and anomaly detection is explored Negative selection is a process of the immune system, which destroys immature antibodies which are capable of recognizing self-antigens Antibodies which survive the negative selection process are self-tolerant and are capable of recognizing almost any foreign body substance Roughly speaking one can say that the negative selection endows the immune system with an ability to distinguish between self and non-self Abstracting the principle of negative selection, the coding antigens as bit-strings which represent network packets or as real-valued n-dimensional points and antibodies as binary detectors or as hyperspheres, one obtains an immune-inspired technique for use in the above mentioned areas of application We are talking about artificial immune systems, when principles and processes of the immune system are abstracted and applied for solving problems In this thesis, we explore the appropriateness of the artificial immune system negative selection for intrusion detection and anomaly detection problems In the first instance, we describe the immune system negative selection principle, and the subsequent the artificial immune system negative selection principe We then describe which network information are required to de- tect an intrusion Results reveal that previous works that apply the negative selection for this application area, are not appropriate for real-world intrusion detection problems Moreover we explore if a different antibody-antigen representations, ie real-valued n-dimensional points and high-dimensional hyperspheres are appropriate for anomaly detection problems The results obtained, reveal that negative selection is not appropriate for anomaly detection problems, especially when compared to statistical anomaly detection methods In summary, we can unfortunately state that negative selection, is not appropriate for network intrusion detection and anomaly detection problems

Optimizing the Performance of an Integrated Process Planning and Scheduling Problem: An AIS-FLC based Approach

Abstract: The present market scenario demands an integration of process planning and scheduling to stay competitive with others. In the present work, an integrated process planning and scheduling model encapsulating the salient features of outsourcing strategy has been proposed. The paper emphasizes on the role of outsourcing strategy in optimizing the performance of enterprises in rapidly changing environment. In the present work authors have proposed an Artificial Immune System based AIS-FLC algorithm embedded with the fuzzy logic controller to solve the complex problem prevailing under such scenario, while simultaneously optimizing the performance. The authors have shown the efficacy of the proposed algorithm by comparing the results with other random search methods.

An immunity-based distributed multiagent-control framework

Abstract: The human immune system is a complex system of cells, molecules, tissues, and diverse organs that can provide us with primary defense against pathogenic organisms. These components are highly interactive and execute the immune response in a coordinated and specific manner. This paper presents a formal mathematical model of an artificial immune system (AIS)-based control framework. The framework aims to provide an integrated solution to control and coordinate complex distributed systems with a large number of autonomous agents such as automated warehouses, distribution centers, and automated material-handling systems. The control framework consists of a set of AIS agents working in response to the changing environment and the occurrence of tasks. The AIS agents manipulate their capabilities to derive appropriate responses to tackle different problems. A methodology describing the response-manipulation algorithm of the AIS agents and their ability to generate new capabilities is discussed in this paper. Through response manipulation and knowledge building, a self-organized and fully distributed system with agents that are able to adapt and accommodate in a dynamic environment via distributed decision making and interagent communication is achieved.

A retrovirus inspired algorithm for virus detection & optimization

Abstract: In the search for a robust and efficient algorithm to be used for computer virus detection, we have developed an artificial immune system genetic algorithm (REALGO) based on the human immune system's use of reverse transcription ribonucleic acid (RNA). The REALGO algorithm provides memory such that during a complex search the algorithm can revert back to and attempt to mutate in a different "direction" in order to escape local minima. In lieu of non-existing virus generic templates, validation is addressed by using an appropriate variety of function optimizations with landscapes believed to be similar to that of virus detection. It is empirically shown that the REALGO algorithm finds "better" solutions than other evolutionary strategies in four out of eight test functions and finds equally "good" solutions in the remaining four optimization problems.

Scheduling Using Artificial Immune System Metaphors: A Review

Abstract: Artificial Immune Systems (AIS) are relatively young emerging techniques, which explore, derive and apply different biologically inspired immune mechanisms, aimed at computational problem solving. Although several researchers have already attempted to adapt such metaphors to production and service scheduling problems, we are not aware of any literature review reporting the use of AIS for scheduling problems. This review of existing scheduling AIS applications shows that the published studies are related to several types of problems: single machine, hybrid and no wait flow shops, job shops, parallel processors. Task allocation and sequencing problems are also addressed in single or multi-objective optimization. After a first part introducing the main principles of artificial immune systems, we summarize how AIS paradigms are used and adapted in existing works to tackle scheduling problems. A discussion is then presented and, finally, several opened research directions are drawn.

Comparison of artificial immune systems and genetic algorithms in electrical engineering optimization

Abstract: Purpose – The purpose of this study is to investigate and compare the ability of a new optimization technique based on the emulation of the immune system to detect the global maximum with multimodal functions and to test the capability of exploring the parameter space with respect to clustering enhanced Genetic Algorithms (GA).Design/methodology/approach – Both algorithms have been tested on analytical test functions and on numerical functions of applicative interest. A set of performance criteria has been defined in order to numerically compare the performances of both optimization strategies.Findings – Results show the great ability of Artificial Immune Systems (AIS) in thoroughly exploring the space of variables. On the other side, GA are faster to converge to the global optimum, but selection pressure can reduce the number of detected local optima.Originality/value – This work is an attempt to assess the performances of a relatively new optimization algorithm based on AIS and to find its behavior on m...

Survey of artificial immune system

Abstract: This paper presents the recent advances in the artificial immune system theory, artificial immune network models, artificial immune algorithms, and applications of artificial immune system. Some future research directions in these areas are also given.

Benefits of Bio-inspired Technologies for Networked Embedded Systems: An Overview

Abstract: The communication between networked embedded systems has become a major research domain in the communication networks area. Wireless sensor networks (WSN) and sensor/actuator networks (SANET) build of huge amounts of interacting nodes build the basis for this research. Issues such as mobility, network size, deployment density, and energy are the key factors for the development of new communication methodologies. Self-organization mechanisms promise to solve scalability problems A¢â‚¬â€œ unfortunately, by decreasing the determinism and the controllability of the overall system. Self-Organization was first studied in nature and its design principles such as feedback loops and the behavior on local information have been adapted to technical systems. Bio-inspired networking is the keyword in the communications domain. In this paper, selected bio-inspired technologies and their applicability for sensor/actuator networks are discussed. This includes for example the artificial immune system, swarm intelligence, and the intercellular information exchange.

Automatable artificial immune system (ais)

Abstract: The present invention relates to methods of constructing an integrated artificial immune system that comprises appropriate in vitro cellular and tissue constructs or their equivalents to mimic the normal tissues that interact with vaccines in mammals. The artificial immune system can be used to test the efficacy of vaccine candidates in vitro and thus, is useful to accelerate vaccine development and testing drug and chemical interactions with the immune system.

Immune anomaly detection enhanced with evolutionary paradigms

Abstract: The paper presents an approach based on principles of immune systems to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of network behavior based on the self-nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. Structures corresponding to self-space are built using a training set from this space. Hyperrectangular detectors covering nonself space are created using niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. Results of experiments show a high quality of intrusion detection, which outperform the quality of recently proposed approach based on hypersphere representation of self-space.

Computational modelling and simulation of the immune system

Abstract: We have developed a software system called SIMISYS that models and simulates aspects of the human immune system based on the computational framework of cellular automata. We model tens of thousands of cells as exemplars of the significant players in the functioning of the immune system, and simulate normal and simple disease situations by interpreting interactions among the cells. SIMISYS 0.3, the current version, models and simulates the innate and adaptive components of the immune system. The specific players we model are the macrophages, dendritic cells, neutrophils, natural killer cells, B cells, T helper cells, complement proteins and pathogenic bacteria.