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Stephanie Forrest
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Journal Articles
Publisher: Journals Gateway
Evolutionary Computation (2005) 13 (2): 179–212.
Published: 01 June 2005
Abstract
View articletitled, A Machine Learning Evaluation of an Artificial Immune System
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for article titled, A Machine Learning Evaluation of an Artificial Immune System
ARTIS is an artificial immune system framework which contains several adaptive mechanisms. LISYS is a version of ARTIS specialized for the problem of network intrusion detection. The adaptive mechanisms of LISYS are characterized in terms of their machine-learning counterparts, and a series of experiments is described, each of which isolates a different mechanism of LISYS and studies its contribution to the system's overall performance. The experiments were conducted on a new data set, which is more recent and realistic than earlier data sets. The network intrusion detection problem is challenging because it requires one-class learning in an on-line setting with concept drift. The experiments confirm earlier experimental results with LISYS, and they study in detail how LISYS achieves success on the new data set.
Journal Articles
Architecture for an Artificial Immune System
UnavailablePublisher: Journals Gateway
Evolutionary Computation (2000) 8 (4): 443–473.
Published: 01 December 2000
Abstract
View articletitled, Architecture for an Artificial Immune System
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for article titled, Architecture for an Artificial Immune System
An artificial immune system (ARTIS) is described which incorporates many properties of natural immune systems, including diversity, distributed computation, error tolerance, dynamic learning and adaptation, and self-monitoring. ARTIS is a general framework for a distributed adaptive system and could, in principle, be applied to many domains. In this paper, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. LISYS is described and shown to be effective at detecting intrusions, while maintaining low false positive rates. Finally, similarities and differences between ARTIS and Holland's classifier systems are discussed.
Journal Articles
Publisher: Journals Gateway
Evolutionary Computation (1993) 1 (3): 191–211.
Published: 01 September 1993
Abstract
View articletitled, Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
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for article titled, Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern-recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of the model. The paper reports simulation experiments on two pattern-recognition problems that are relevant to natural immune systems. Finally, it reviews the relation between the model and explicit fitness-sharing techniques for genetic algorithms, showing that the immune system model implements a form of implicit fitness sharing.
Journal Articles
Publisher: Journals Gateway
Evolutionary Computation (1993) 1 (2): 127–149.
Published: 01 June 1993
Abstract
View articletitled, Searching for Diverse, Cooperative Populations with Genetic Algorithms
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for article titled, Searching for Diverse, Cooperative Populations with Genetic Algorithms
In typical applications, genetic algorithms (GAs) process populations of potential problem solutions to evolve a single population member that specifies an ‘optimized’ solution. The majority of GA analysis has focused on these optimization applications. In other applications (notably learning classifier systems and certain connectionist learning systems), a GA searches for a population of cooperative structures that jointly perform a computational task. This paper presents an analysis of this type of GA problem. The analysis considers a simplified genetics-based machine learning system: a model of an immune system. In this model, a GA must discover a set of pattern-matching antibodies that effectively match a set of antigen patterns. Analysis shows how a GA can automatically evolve and sustain a diverse, cooperative population. The cooperation emerges as a natural part of the antigen-antibody matching procedure. This emergent effect is shown to be similar to fitness sharing, an explicit technique for multimodal GA optimization. Further analysis shows how the GA population can adapt to express various degrees of generalization. The results show how GAs can automatically and simultaneously discover effective groups of cooperative computational structures.