The evolution and analysis of potential antibody library for use in job-shop scheduling
New ideas in optimization
Deriving a concise description of non-self patterns in an aritificial immune system
New learning paradigms in soft computing
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
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
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
An immunological approach to change detection: algorithms, analysis and implications
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
A formal framework for positive and negative detection schemes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Immune anomaly detection enhanced with evolutionary paradigms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
A hybrid approach for learning concept hierarchy from Malay text using artificial immune network
Natural Computing: an international journal
Gene libraries: coverage, efficiency and diversity
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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Artificial Immune Systems (AIS) have been shown to be useful, practical and realisable approaches to real-world problems. Most AIS implementations are based around a canonical algorithm such as clonotypic learning, which we may think of as individual, lifetime learning. Yet a species also learns. Gene libraries are often thought of as a biological mechanism for generating combinatorial diversity of antibodies. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding the lifetime learning mechanisms. Over time, the gene libraries in a species will evolve to an appropriate bias for the expected environment (based on species memory). Thus gene libraries are a form of meta-learning which could be useful for AIS. Yet they are hardly ever used. In this paper we consider some of the possible benefits and implications of incorporating the evolution of gene libraries into AIS practice. We examine some of the issues that must be considered if the implementation is to be successful and beneficial.