Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Sparse Distributed Memory
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
Hints for Adaptive Problem Solving Gleaned from Immune Networks
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Case Memory and Retrieval Based on the Immune System
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Multi-class iteratively refined negative selection classifier
Applied Soft Computing
Neuro-immune-endocrine (NIE) models for emergency services interoperatibility
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
A novel negative selection algorithm with an array of partial matching lengths for each detector
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Gene libraries: coverage, efficiency and diversity
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
What have gene libraries done for AIS?
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Hybrid negative selection approach for anomaly detection
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
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From a computer science perspective the immune system is a complex, self organizing and highly distributed system that has no centralized control and uses learning and memory when solving particular tasks. The learning process does not require negative examples and the acquired knowledge is represented in explicit form. The main actors of the immune systems are lymphocytes equipped with a set of receptors recognizing intruders, or pathogens (i.e. viruses, bacteria, etc.). Because the receptors on a surface of a single lymphocyte are of identical structure, and they recognize only a narrow class of pathogens, we can treat them as a single receptor from an abstract point of view. In this paper we focus on a binary AIS in which all the information is represented by the bit string, and as the match rule we take the k-contiguous bits rule: two strings match if they have the same bits in at least k contiguous positions. With such a method of receptor activation we study the number of strings recognized by a single receptor is proposed, and next we investigate how this number decreases when the size of the repertoire increases. Second, we give a method enabling to determine the number of strings which cannot be detected by an "ideal" repertoire in the presence of a set of self strings (i.e. strings representing normal behavior of a system). These results are of importance when constructing an optimal receptors repertoire.