Open Access
Application areas of AIS: the past, present and future.
Emma Hart,Jon Timmis +1 more
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TLDR
In this paper, the authors take a step back and reflect on the contributions that the Artificial Immune Systems (AIS) has brought to the application areas to which it has been applied, and suggest a set of problem features that they believe will allow the true potential of the immunological system to be exploited in computational systems.Abstract:
After a decade of research into the area of artificial immune systems, it is worthwhile to take a step back and reflect on the contributions that the
paradigm has brought to the application areas to which it has been applied. Undeniably, there have been a lot of successful stories—however, if the
field is to advance in the future and really carve out its own distinctive niche, then it is necessary to be able to illustrate that there are clear benefits to
be obtained by applying this paradigm rather than others. This paper attempts to take stock of the application areas that have been tackled in the
past, and ask the difficult question ‘‘was it worth it ?’’. We then attempt to suggest a set of problem features that we believe will allow the true
potential of the immunological system to be exploited in computational systems, and define a unique niche for AISread more
Citations
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Proceedings ArticleDOI
Immune Generalized Differential Evolution for dynamic multiobjective optimization problems
TL;DR: The proposed algorithm combines the ideas of the generalized differential evolution and the artificial immune system to create a hybrid algorithm which uses the advantages of both approaches to solve dynamic multiobjective optimization problems (DMOPs).
Journal Article
Recognition of plant leaves using the dendritic cell algorithm
TL;DR: The experimental results show that the dendritic cell algorithm, from the danger theory, as a classifier and the wavelet transform as a features space, is feasible and it gives an interesting recognition rate.
Journal ArticleDOI
Modifying Regeneration Mutation and Hybridising Clonal Selection for Evolutionary Algorithms Based Timetabling Tool
TL;DR: This research developed a new approach which modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced.
Journal ArticleDOI
Artificial Immunology for Collective Adaptive Systems Design and Implementation
TL;DR: This work proposes a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system, and shows that it enables self- expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures.
Dissertation
Distributed Reinforcement Learning for Network Intrusion Response
TL;DR: Coordinated Team Learning (CTL) is proposed which is a novel design to the original Multiagent Router Throttling approach based on the divide-and-conquer paradigm, that uses task decomposition and coordinated team rewards to better scale-up CTL, combined with a form of reward shaping.
References
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