Computer
Physical model of immune inspired computing
Information Sciences—Informatics and Computer Science: An International Journal
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Using the danger model of immune systems for distributed defense in modern data networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Modelling danger and anergy in artificial immune systems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A self-adaptive migration model genetic algorithm for data mining applications
Information Sciences: an International Journal
Sensing danger: Innate immunology for intrusion detection
Information Security Tech. Report
Designing of classifiers based on immune principles and fuzzy rules
Information Sciences: an International Journal
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
Automatic knot adjustment using an artificial immune system for B-spline curve approximation
Information Sciences: an International Journal
A multi-modal immune algorithm for the job-shop scheduling problem
Information Sciences: an International Journal
Definition Method of Danger Signal Based on Genetic Optimization
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
Information Sciences: an International Journal
Artifical Immune Systems, Danger Theory, and the Oracle Problem
TAIC-PART '09 Proceedings of the 2009 Testing: Academic and Industrial Conference - Practice and Research Techniques
A transitional view of immune inspired techniques for anomaly detection
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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The traditional immune algorithm (IA) is based on a self-nonself biological immunity mechanism. Recently, a novel immune theory called the danger model theory has provided more suitable biological information for data handling compared with the self-nonself mechanism. According to the danger model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model Immune Algorithm (DMIA) that differs from the traditional IA in terms of the self-nonself biological immunity mechanism. We define a danger area and a danger signal in DMIA. We use the selection, mutation, and specific danger operators to update the population. The algorithm can achieve complex problem optimization. Simulation studies demonstrate that DMIA exhibits a higher efficiency than traditional genetic algorithms and other algorithms when considering a number of complicated functions.