Schema recombination in pattern recognition problems
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
APL '95 Proceedings of the international conference on Applied programming languages
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Computing in Science and Engineering
Genetic Algorithms and the Immune System
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Sufficiency verification of HIV-1 pathogenesis based on multi-agent simulation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Simulating antigenic drift and shift in influenza A
Proceedings of the 2009 ACM symposium on Applied Computing
The swarming body: simulating the decentralized defenses of immunity
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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Currently most reported immune system simulations in literature involve the use of differential equations, genetic algorithm-based searching or simple cellular automata models. This limits the diversity in results obtained and thus provides fewer avenues for experimenting with behavioral responses of the immune system entities under exogenous stimulations. Complex adaptive systems (or CAS) by Holland provide a way of modeling natural systems with complex aggregation and nonlinear interactions to exhibit emergent behaviours. The immune system, being a powerful and flexible information processing system is particularly suited to being modeled using CAS. This paper describes a Java-based implementation of a framework for modeling the immune system, particularly Human Immunodeficiency Virus (or HIV) attack, using a CAS model. The credibility of the system is established through comparisons against available viral dynamics data. We show that it is feasible to achieve relatively accurate predictions of viral pathogenesis through agent-based discrete event simulations, the first steps towards improved automation of hypothesis verification.