Showing papers on "Artificial immune system published in 1997"

Immunology for physicists

Abstract: The immune system is a complex system of cells and molecules that can provide us with a basic defense against pathogenic organisms. Like the nervous system, the immune system performs pattern recognition tasks, learns, and retains a memory of the antigens that it has fought. The immune system contains more than 10{sup 7} different clones of cells that communicate via cell-cell contact and the secretion of molecules. Performing complex tasks such as learning and memory involves cooperation among large numbers of components of the immune system and hence there is interest in using methods and concepts from statistical physics. Furthermore, the immune response develops in time and the description of its time evolution is an interesting problem in dynamical systems. In this paper, the authors provide a brief introduction to the biology of the immune system and discuss a number of immunological problems in which the use of physical concepts and mathematical methods has increased our understanding. {copyright} {ital 1997} {ital The American Physical Society}

Immunity-based systems: a survey

Abstract: Biological systems such as human beings can be regarded as sophisticated information processing systems, and can be expected to provide inspiration for various ideas to science and engineering. Biologically-motivated information processing systems can be classified into: brain-nervous systems (neural networks), genetic systems (evolutionary algorithms), and immune systems (artificial immune systems). Among these, nervous systems and genetic systems have been widely applied to various fields. There have been a relative few applications of the immune system. This paper presents a survey of artificial immune systems and provides numerous insights of immunity-based systems applications in science and engineering.

Artificial neural networks and artificial immune systems: similarities and differences

Abstract: Both the nervous system and the immune system are complex biological systems. Recognition and categorization are the major functions of both systems. The information processing principles of these natural systems inspired in developing intelligent problem-solving techniques, namely, the artificial neural network (ANN) and the artificial immune system (AIS). Though ANNs are well established techniques and are widely used, AISs have received very little attention and there have been relatively few applications of the AIS. This paper briefly describes some of the similarities and differences of these two systems from a computational viewpoint. The paper also reports some preliminary comparative results of the artificial systems.

Ga based simulation of immune networks applications in structural optimization

Abstract: Genetic algorithms have received considerable recent attention in the optimal design of structural systems. These algorithms derive a computational leverage from an intrinsic pattern recognition capability, whereby patterns or schemata associated with a high level of fitness are identified and evolved at a near-exponential growth rate through generations of simulated evolution. This highly exploitative search process has been shown to be extremely effective in searching for schema that represent an optimum, requiring only that an appropriate measure of fitness be defined. This exploitative pattern recognition process is also at work in another biological system - the immune system responsible for recognizing antigens foreign to the system and generating antibodies to combat the growth of these antigens. The paper describes key elements of how the functioning of the immune system can be modelled in the context of genetic search. It then provides an overview of the implications of this model in improving th...

Artificial immune systems and aerial image segmentation

Abstract: This paper describes our initial investigations in applying artificial immune systems to feature segmentation in remotely sensed images. The current generation of commercial imaging satellites provides increased opportunities for automated image analysis due to the large volume of high resolution imagery they will produce. Artificial immune systems (AIS) are successful in other pattern recognition tasks and have several similarities to the aerial image classification problem. We use an AIS for road pixel identification and observe several areas for further development.

Emergent construction of artificial immune networks for autonomous mobile robots

Abstract: In the behavior-based artificial intelligence (AI) approach, there are the following problems that have to be resolved: how do we construct an appropriate arbitration mechanism, and how do we prepare appropriate competence modules. Therefore, we have been investigating a new behavior arbitration mechanism based on the biological immune system. However, we have not found a solution to the latter problem. In this paper, we try to incorporate the metadynamics function and selection mechanism into our previously proposed method in order to construct an appropriate immune network without human intervention.

Application of an immune algorithm to multi-optimization problems

Abstract: An immune system has powerful abilities such as memory, recognition, and learning to respond to invading antigens. The concept is expected to be applicable to many engineering applications. This paper proposes an optimization algorithm that imitates the immune system in order to solve multi-optimization problems partly by using a genetic algorithm. Through illustrative examples of multimodal functions such as the Shubert function, the proposed algorithm is shown to be effective in searching for a set of global solutions. © 1998 Scripta Technica. Electr Eng Jpn, 122(2): 30–37, 1998

A DNA based artificial immune system for self-nonself discrimination

Abstract: Artificial immune systems attempt to distinguish self from nonself through string matching operations. A detector set of strings is selected by eliminating random strings that match the self strings. DNA based computers have been proposed to solve complex problems that defy solution on conventional computers. They are based on (hydrogen bonding based) matchings (called hybridizations) between Watson-Crick complementary pairs, Adenine-Thymine or Cytosine-Guanine. Therefore, a single strand (an oligonucleotide) will bind with other oligonucleotides that match most closely its sequence under the operation of Watson-Crick complementation. In this paper, an algorithm for implementing an artificial immune system for self-nonself discrimination based on DNA is described. This procedure takes advantage of the inherent pattern matching capability of DNA hybridization reactions and the notion of similarity naturally found in DNA hybridization.

Immunized adaptive critics for level 2 intelligent control

Abstract: Immunized computational systems combine a priori knowledge with the adapting capabilities of immune systems to provide a powerful alternative to currently available techniques for intelligent control. In this paper, we present a perspective on various levels of intelligent control and relate them to similar functioning in human immune systems. A technique for implementing immunized computational systems as adaptive critics is presented and the technique is then applied to a simple two degree-of-freedom control problem as well as a more complex flight path generator for level 2, nonlinear, full-envelope, intelligent aircraft control.

The immune system as a prototype of autonomous decentralized systems

Abstract: Some of the salient biology is reviewed, so that it can be seen how suitable the immune system is as a prototype of "bottom up" artificial intelligence. It is stressed that the immune system is entirely distributed (to first approximation). Its agents, the cells, are highly complex. Much is known about these cells and their interaction with each other and with pathogens, and the system is of high biological and medical interest. Major questions to be addressed include the following: 1) what are the "goals" of the immune system, and how can feedback promote these goals? 2) how can spatial organization allow non-specific chemical signals to select specific immune elements that contribute effectively to system goals? 3) how and in what sense can immune system performance be improved in the absence of an overall goal? and 4) how does the immune system compare with other autonomous decentralized systems.

Immunized adaptive critics

Abstract: Immunized computational systems combine a priori knowledge with the adapting capabilities of immune systems to provide a powerful alternative to currently available techniques for intelligent control. In this paper, we present our perspective on various levels of intelligent control and relate them to similar functioning in human immune systems. A technique for implementing immunized computational systems as adaptive critics is presented and the technique is then applied to a control problem to document the effectiveness of this approach.