Showing papers on "Artificial immune system published in 2004"
01 Jan 2004
TL;DR: The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004.
Abstract: The two volume set LNCS 3102/3103 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, held in Seattle, WA, USA, in June 2004. The 230 revised full papers and 104 poster papers presented were carefully reviewed and selected from 460 submissions. The papers are organized in topical sections on artificial life, adaptive behavior, agents, and ant colony optimization; artificial immune systems, biological applications; coevolution; evolutionary robotics; evolution strategies and evolutionary programming; evolvable hardware; genetic algorithms; genetic programming; learning classifier systems; real world applications; and search-based software engineering.
913 citations
TL;DR: Experimental results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS, which is an immune-inspired supervised learning algorithm.
Abstract: This paper presents the inception and subsequent revisions of an immune-inspired supervised learning algorithm, Artificial Immune Recognition System (AIRS). It presents the immunological components that inspired the algorithm and describes the initial algorithm in detail. The discussion then moves to revisions of the basic algorithm that remove certain unnecessary complications of the original version. Experimental results for both versions of the algorithm are discussed and these results indicate that the revisions to the algorithm do not sacrifice accuracy while increasing the data reduction capabilities of AIRS.
380 citations
TL;DR: Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving HFS problems.
264 citations
01 Aug 2004
TL;DR: This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and swarm systems, and brings together novel and philosophical trends in the exciting fields of artificial life and robotics.
Abstract: Recent Developments in Biologically Inspired Computing is necessary reading for undergraduate and graduate students, and researchers interested in knowing the most recent advances in problem-solving techniques inspired by nature. This book covers the most relevant areas in computational intelligence, including evolutionary algorithms, artificial neural networks, artificial immune systems and swarm systems. It also brings together novel and philosophical trends in the exciting fields of artificial life and robotics. This book has the advantage of covering a large number of computational approaches, presenting the state-of-the-art before entering into the details of specific extensions and new developments. Pseudocodes, flow charts and examples of applications are provided of the new approaches presented.
221 citations
13 Sep 2004
TL;DR: The use of artificial immune systems in intrusion detection is an appealing concept for two reasons: Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner; Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security as discussed by the authors.
Abstract: The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research.
169 citations
01 Nov 2004
TL;DR: This survey explores the salient features of the immune system that are inspiring computer scientists and engineers to build Artificial Immune Systems (AIS), and an extensive survey of applications is presented, ranging from network security to optimisation and machine learning.
Abstract: The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational point of view, the immune system has much to offer by way of inspiration to computer scientists and engineers alike. As computational problems become more complex, increasingly, people are seeking out novel approaches to these problems, often turning to nature for inspiration. A great deal of attention is now being paid to the vertebrate immune system as a potential source of inspiration, where it is thought that different insights and alternative solutions can be gleaned, over and above other biologically inspired methods.
147 citations
TL;DR: The results indicate that the proposed algorithm to handle constraints of all types in a genetic algorithm used for global optimization is highly competitive with respect to penalty-based techniques and withrespect to other constraint-handling techniques which are considerably more complex to implement.
Abstract: This paper proposes an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality, and inequality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using several test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.
133 citations
TL;DR: Experiments indicate that specific memory might also exist in the innate immune systems of invertebrates, and the underlying mechanisms are unknown; yet such phenomenological evidence is relevant for understanding the principles and evolution of immune defence.
112 citations
13 Sep 2004
TL;DR: A simple parallel version of the classification algorithm Artificial Immune Recognition System (AIRS) is presented and initial results indicate that a decrease in overall runtime can be achieved through fairly naive techniques.
Abstract: The mammalian immune system is a highly complex, inherently parallel, distributed system. The field of Artificial Immune Systems (AIS) has developed a wide variety of algorithms inspired by the immune system, few of which appear to capitalize on the parallel nature of the system from which inspiration was taken. The work in this paper presents the first steps at realizing a parallel artificial immune system for classification. A simple parallel version of the classification algorithm Artificial Immune Recognition System (AIRS) is presented. Initial results indicate that a decrease in overall runtime can be achieved through fairly naive techniques. The need for more theoretical models of the behavior of the algorithm is discussed.
97 citations
10 Oct 2004
TL;DR: This paper gives a concise survey on the recent progresses of the theory as well as applications of the AIO schemes, in which some representative approaches are briefly introduced and discussed.
Abstract: Inspired by natural immune systems, artificial immune systems (AIS) are an emerging kind of computational intelligence paradigm. During the past decade, the AIS have gained great research interest in wide engineering fields. Artificial immune optimization (AIO) methods are an important partner of the AIS. They have been successfully applied to deal with numerous challenging optimization problems with superior performance over classical optimization techniques. This paper gives a concise survey on the recent progresses of the theory as well as applications of the AIO schemes, in which some representative approaches are briefly introduced and discussed.
86 citations
TL;DR: The use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR is investigated and the goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior.
Abstract: In mobile ad-hoc networks, nodes act both as terminals and information relays, and participate in a common routing protocol, such as Dynamic Source Routing (DSR). The network is vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. In this paper we investigate the use of an Artificial Immune System (AIS) to detect node misbehavior in a mobile ad-hoc network using DSR. The system is inspired by the natural immune system of vertebrates. Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior. We describe the first step of our design; it employs negative selection, an algorithm used by the natural immune system. We define how we map the natural immune system concepts such as self, antigen and antibody to a mobile ad-hoc network, and give the resulting algorithm for misbehavior detection. We implemented the system in the network simulator Glomosim; we present detection results and discuss how the system parameters impact the results. Further steps will extend the design by using an analogy to the innate system, danger signals, costimulation and memory cells.
13 Sep 2004
TL;DR: An Artificial Immune System (AIS) is used, a system inspired by the human immune system, to build a system that automatically learns and detects new misbehavior in mobile ad-hoc networks.
Abstract: In mobile ad-hoc networks, nodes act both as terminals and information relays, and they participate in a common routing protocol, such as Dynamic Source Routing (DSR). The networks are vulnerable to routing misbehavior, due to faulty or malicious nodes. Misbehavior detection systems aim at removing this vulnerability. For this purpose, we use an Artificial Immune System (AIS), a system inspired by the human immune system (HIS). Our goal is to build a system that, like its natural counterpart, automatically learns and detects new misbehavior.
18 Sep 2004
TL;DR: In this article, a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems is proposed, which consists of two parts: a sequential covering procedure and a rule evolution procedure.
Abstract: This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm.
13 Sep 2004
TL;DR: It is proposed that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and a framework for such a framework is outlined here, in thecontext of AIS network models.
Abstract: We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of AIS network models. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.
TL;DR: This article presents a parallel image processing system based on the concept of reactive agents in the oRis language, which allows to describe finely and simply the agents’ behaviors to detect image features.
26 Jun 2004
TL;DR: The work presented in this paper results from an investigation into the opt-aiNET algorithm, a well-known immune inspired algorithm for function optimisation, which identifies two minor errors within the code and proposes a slight augmentation of the algorithm to automate the process of peak identification.
Abstract: Verifying the published results of algorithms is part of the usual research process. This helps to both validate the existing literature, but also quite often allows for new insights and augmentations of current systems in a methodological manner. This is very pertinent in emerging new areas such as Artificial Immune Systems, where it is essential that any algorithm is well understood and investigated. The work presented in this paper results from an investigation into the opt-aiNET algorithm, a well-known immune inspired algorithm for function optimisation. Using the original source code developed for opt-aiNET, this paper identifies two minor errors within the code, propose a slight augmentation of the algorithm to automate the process of peak identification: all of which affect the performance of the algorithm. Results are presented for testing of the existing algorithm and in addition, for a slightly modified version, which takes into account some of the issues discovered during the investigations.
19 Jun 2004
TL;DR: The initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation show that the opt-aiNET algorithm, when compared to the B-cell algorithm and hybrid GA, takes longer to find the solution, without necessarily a better quality solution.
Abstract: Do artificial immune systems (AIS) have something to offer the world of optimisation? Indeed do they have any new to offer at all? This paper reports the initial findings of a comparison between two immune inspired algorithms and a hybrid genetic algorithm for function optimisation. This work is part of ongoing research which forms part of a larger project to assess the performance and viability of AIS. The investigation employs standard benchmark functions, and demonstrates that for these functions the opt-aiNET algorithm, when compared to the B-cell algorithm and hybrid GA, on average, takes longer to find the solution, without necessarily a better quality solution. Reasons for these differences are proposed and it is acknowledged that this is preliminary empirical work. It is felt that a more theoretical approach may well be required to ascertain real performance and applicability issues.
19 Jun 2004
TL;DR: This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.
Abstract: The AIRS classifier, based on principles derived from resource limited artificial immune systems, performs consistently well over a broad range of classification problems. This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.
29 Nov 2004
TL;DR: In this paper, an artificial immune system based optimization approach for solving the economic dispatch problem in a power system is presented, which is easy to implement, converged within an acceptable execution time and highly optimal solution for economic dispatch with minimum generation cost can be achieved.
Abstract: This paper presents an artificial immune system based optimization approach for solving the economic dispatch problem in a power system. Economic dispatch determines the electrical power to be generated by the committed generating units in a power system so that the generation cost is minimised, while satisfying the load demand simultaneously. The developed artificial immune system optimization technique used the total generation cost as the objective function and represented as the affinity measure. Through genetic evolution, the antibodies with high affinity measure are produced and become the solution. The simulation results reveal that the developed technique is easy to implement, converged within an acceptable execution time and highly optimal solution for economic dispatch with minimum generation cost can be achieved. The result also confirms that AIS based optimization technique can be a useful tool for solving optimal solution in economic dispatch problem, which involves a large number of generating units and at the same time to comply with a large number of constraints.
13 Sep 2004
TL;DR: An architecture for a robot control is proposed which is based on the requirements from the RoboCup and AAAI Rescue Robot Competition, and an artificial immune system comprises the core component.
Abstract: An architecture for a robot control is proposed which is based on the requirements from the RoboCup and AAAI Rescue Robot Competition. An artificial immune system comprises the core component. The suitability of this architecture for the competition and related scenarios, including the modelling of the environment, was verified by simulation.
25 Aug 2004
TL;DR: This paper describes a new artificial immune system algorithm for data clustering that resembles the CLONALG, widely used AIS algorithm but much simpler as it uses one shot learning and omits cloning.
Abstract: This paper describes a new artificial immune system algorithm for data clustering. The proposed algorithm resembles the CLONALG, widely used AIS algorithm but much simpler as it uses one shot learning and omits cloning. The algorithm is tested using four simulated and two benchmark data sets for data clustering. Experimental results indicate it produced the correct clusters for the data sets.
27 Oct 2004
TL;DR: This paper aims to minimize the effect of ‘Curse of Dimensionality’ problem in shape-space representation by developing an Attribute Weighted Artificial Immune System (AWAIS), and reaches 100% classification accuracy with only a few numbers of network units.
Abstract: ‘Curse of Dimensionality’ problem in shape-space representation which is used in many network-based Artificial Immune Systems (AISs) affects classification performance at a high degree. In this paper, to increase classification accuracy, it is aimed to minimize the effect of this problem by developing an Attribute Weighted Artificial Immune System (AWAIS). To evaluate the performance of proposed system, aiNet, an algorithm that have a considerably important place among network-based AIS algorithms, was used for comparison with our developed algorithm. Two artificial data sets used in aiNet, Two-spirals data set and Chainlin k data set were applied in the performance analyses, which led the results of classification performance by means of represented network units to be higher than aiNet. Furthermore, to evaluate performance of the algorithm in a real world application, wine data set that taken from UCI Machine Learning Repository is used. For the artificial data sets, proposed system reached 100% classification accuracy with only a few numbers of network units and for the real world data set, wine data set, the algorithm obtained 98.23% classification accuracy which is very satisfying result if it is considered that the maximum classification accuracy obtained with other systems is 98.9%.
TL;DR: A hierarchical strategy is developed to extract texture objects according to their roughness and an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy.
Book•
01 Jan 2004
TL;DR: The Immune System as a Self-Defining Process, Information Flow, Biological Field, and Autonomous Distributed Systems, and information flow, biological field, and autonomous distributed systems are studied.
Abstract: 1 Introduction.- 2 Toward a Systems Science for Biological Systems.- 3 The Immune System as an Information System.- 4 Defining Immunity-Based Systems.- 5 A Self-Organizing Network Based on the Concept of the Immune Network.- 6 Sensor Networks Using the Self-Organizing Network.- 7 A Multiagent Framework Learned from the Immune System.- 8 An Application of the Immune Algorithm with an Agent Framework.- 9 Information Flow, Biological Field, and Autonomous Distributed Systems.- 10 The Immune System as a Self-Defining Process.- 11 Conclusions.- References.
13 Sep 2004
TL;DR: A Reactive Immune Network (RIN) is proposed and applied to intelligent mobile robot learning navigation strategies within unknown environments and shows that the robot is capable to avoid obstacles, escape traps, and reach goal effectively.
Abstract: In this paper, a Reactive Immune Network (RIN) is proposed and applied to intelligent mobile robot learning navigation strategies within unknown environments Rather than building a detailed mathematical model of immune systems, we try to explore the principle in immune network focusing on its self-organization, adaptive learning capability and immune memory Modified virtual target method is integrated to solve local minima problem Several trap situations designed by early researchers are employed to evaluate the performance of the proposed immunized architecture Simulation results show that the robot is capable to avoid obstacles, escape traps, and reach goal effectively
26 Jun 2004
TL;DR: This paper describes how the design of AIS-based IDSs can be improved through the use of evolutionary hackers in the form of GENERTIA red teams (GRTs) to discover holes found in the immune system.
Abstract: Artificial Immune Systems (AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems (IDSs). This paper describes how the design of AIS-based IDSs can be improved through the use of evolutionary hackers in the form of GENERTIA red teams (GRTs) to discover holes (in the form of type II errors) found in the immune system. GENERTIA is an interactive tool for the design and analysis of immunity-based intrusion detection systems. Although the research presented in this paper focuses on AIS-based IDSs, the concept of GENERTIA and red teams can be applied to any IDS that uses machine learning techniques to develop models of normal and abnormal network traffic. In this paper we compare a genetic hacker with six evolutionary hackers based on particle swarm optimization (PSO). Our results show that genetic and swarm search are effective and complementary methods for vulnerability analysis. Our results also suggest that red teams based on genetic/PSO hybrids (which we refer to Genetic Swarms) may hold some promise.
TL;DR: Based on the Antibody Clonal Selection Theory of immunology, a novel artificial immune system algorithm, adaptive dynamic clone select algorithm, is put forward that prevents prematurity more effectively and has high convergence speed.
Abstract: Based on the Antibody Clonal Selection Theory of immunology, a novel artificial immune system algorithm, adaptive dynamic clone select algorithm, is put forward. The new algorithm is intended to integrate the local searching with the global and the probability evolution searching with the stochastic searching. Compared with the improved genetic algorithm and other clonal selection algorithms, the new algorithm prevents prematurity more effectively and has high convergence speed. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.
29 Nov 2004
TL;DR: In this paper, a technique to determine the location of the SVC in order to minimize the transmission losses in a power system can be minimized by means of reactive power compensation Installing static Var compensation (SVC) has known to be able to improve voltage level in the system and hence minimizing the system losses.
Abstract: Loss minimization in power system is an important consideration research issue Transmission losses in a power system can be minimized by means of reactive power compensation Installing static Var compensation (SVC) in a power system has known to be able to improve voltage level in the system and hence minimizing the system losses This paper presents a technique to determine the location of the SVC in order to minimize loss in the system The performance of this technique is tested using 14 buses IEEE reliability test system A load flow programmed written in MATLAB by using artificial immune system (AIS) technique was used to compute power flow The test result shows that the location and sizing of the SVC identified by the proposed technique has been able to improve the voltage level of the system and also minimize the losses
13 Sep 2004
TL;DR: The Immune System is a complex adaptive system containing many details and many exceptions to established rules that create difficulties for the study of immunology but also give hints to how artificial immune systems may be designed.
Abstract: The Immune System is a complex adaptive system containing many details and many exceptions to established rules Exceptions such as the suppression effect that causes T-cells to develop reversible aggressive and tolerant behaviors create difficulties for the study of immunology but also give hints to how artificial immune systems may be designed
19 Jun 2004
TL;DR: The results of experiment show that, the proposed fast immunized evolutionary programming can improve not only the convergent speed of original algorithm but also the computation effect of original algorithms, and is a very good optimization method.
Abstract: Evolutionary programming is a good global optimization method. By introduction, the improved adaptive mutation operation and improved selection operation based on thickness adjustment of artificial immune system into traditional evolutionary programming, a fast immunized evolutionary programming is proposed in this paper. At last, this algorithm is verified by simulation experiment of typical optimization function. The results of experiment show that, the proposed fast immunized evolutionary programming can improve not only the convergent speed of original algorithm but also the computation effect of original algorithm, and is a very good optimization method.