Natural immune systems protect animals from dangerous foreign pathogens, including bacteria, viruses, parasites, and toxins. Their role in the body is analogous to that of computer security systems in computing. Although there are many differences between living organisms and computer systems, this article argues that the similarities are compelling and could point the way to improved computer security. Improvements can be achieved by designing computer immune systems that have some of the important properties illustrated by natural immune systems. These include multi-layered protection, highly distributed detection and memory systems, diversity of detection ability across individuals, inexact matching strategies, and sensitivity to most new foreign patterns. We first give an overview of how the immune system relates to computer security. We then illustrate these ideas with two examples.

Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

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.

This thesis explores the design and application of artificial immune systems (AISs), problem-solving systems inspired by the human and other immune systems. AISs to date have largely been modelled on the biological adaptive immune system and have taken little inspiration from the innate immune system. The first part of this thesis examines the biological innate immune system, which controls the adaptive immune system. The importance of the innate immune system suggests that AISs should also incorporate models of the innate immune system as well as the adaptive immune system. This thesis presents and discusses a number of design principles for AISs which are modelled on both innate and adaptive immunity. These novel design principles provided a structured framework for developing AISs which incorporate innate and adaptive immune systems in general. These design principles are used to build a software system which allows such AISs to be implemented and explored.

Abstract. An important problem in the theory of finite dynamical systems is to link the structure of a system with its dynamics. This paper contains such a link for a family of nonlinear systems over the field with two elements. For systems that can be described by monomials (including Boolean AND systems), one can obtain information about the limit cycle structure from the structure of the monomials. In particular, the paper contains a sufficient condition for a monomial system to have only fixed points as limit cycles. This condition depends on the cycle structure of the dependency graph of the system and can be verified in polynomial time. 1.

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We have constructed a stochastic stage-structured model of the cytotoxic T lymphocyte (CTL) response to antigen and the maintenance of immunological memory. The model follows the the dynamics of a viral infection and the stimulation, proliferation, and differentiation of naive CD8+ T cells into effector CTL, which can eliminate virally infected cells. The model is capable of following the dynamics of multiple T cell clones, each with a T cell receptor represented by a digit string. MHC-viral peptide complexes are also represented by strings and a string match rule is used to compute the affinity of a T cell receptor for a viral epitope. The avidities of interactions are also computed by taking into consideration the density of MHC-viral peptides on the surface of an infected cell. Lastly, the model allows the probability of T cell stimulation to depend on avidity but also incorporates the notion of an antigen-independent programmed proliferative response. We compare the model to experimental data on the cytotoxic T cell response to lymphocytic choriomeningitis virus infections.

The biological immune system exhibits powerful information processing capabil-ities, and therefore is of great interest to the computer scientist. A rapidly expanding research area has attempted to model many of the features inherent in the natural im-mune system in order to solve complex computational problems. This thesis examines the metaphor in detail, in an effort to understand and capitalise on those features of the metaphor which distinguish it from other existing methodologies. Two problem domains are considered — those of scheduling and data-clustering. It is argued that these domains exhibit similar characteristics to the environment in which the biological immune system operates and therefore that they are suitable candidates for application of the metaphor. For each problem domain, two distinct models are developed, incor-porating a variety of immunological principles. The models are tested on a number of artifical benchmark datasets. The success of the models on the problems considered confirms the utility of the metaphor. i

Abstract. Many complex systems can be modeled as multiagent systems in which the constituent entities (agents) interact with each other. The global dynamics of such a system is determined by the nature of the local interactions among the agents. Since it is difficult to formally analyze complex multiagent systems, they are often studied through computer simulations. While computer simulations can be very useful, results obtained through simulations do not formally validate the observed behavior. Thus, there is a need for a mathematical framework which one can use to represent multiagent systems and formally establish their properties. This work contains a brief exposition of some known mathematical frameworks that can model multiagent systems. The focus is on one such framework, namely that of finite dynamical systems. Both, deterministic and stochastic versions of this framework are discussed. The paper contains a sampling of the mathematical results from the literature to show how finite dynamical systems can be used to carry out a rigorous study of the properties of multiagent systems and it is shown how the framework

Abstract. In this paper we present an Abstract Immune System Algorithm, based on the model introduced by Farmer et al, inspired on the theory of Clonal Selection and Idiotypic Network due to Niels Jerne. The proposed algorithm can be used in order to solve problems much in the way that Evolutionary Algorithms or Artificial Neural Networks do. Besides presenting the Algorithm itself, we briefly discuss its various parameters, how to encode input data and how to extract the output data from its outcome. The reader can do their own experiments using the workbench found in the address